This week I interviewed my very good friend, Spencer Greenberg, who just published a blockbuster book with his co-author, clinical psychologist Jeremy Stevenson. Their new book is The 12 Levers: The Complete Psychological Toolkit for Improving Your Life. I also created a comprehensive AI skill to help you apply the book’s lessons in your day-to-day life. Whenever you trigger the skill or talk to AI about something the book could be helpful for, the skill helps you work through one of the book’s recommended interventions.

Interview Overview

Every year, 15,000 newly published self-help books promise people better lives if they apply the author’s techniques, triggering another round of fads. From waking up early to cold plunges to mastering habit protocols to adopting mindsets like Stoicism, the number of things we could do to improve our lives seems to be endless.

Counterintuitively, the sheer volume of techniques can have the opposite of the intended result. This can lead to overwhelm, FOMO, and anxiety. Put differently, the scarce resource is no longer information. It’s a trustworthy map, and the judgment to know which move a given moment calls for.

The 12 Levers is the first serious attempt I’ve seen to compress the entire territory of self-improvement into a map you can hold in your head, built by the people best positioned to grade the evidence honestly. (True to form, the book ends with an appendix ranking the evidence behind each lever, including where it’s thin.)

If you’re interested in finding the signal in the noise so you can focus on the few things that matter for you, then you’ll want to hear this interview and purchase Spencer and Jeremy’s book.

About The Authors

Spencer is one of the most rigorous and systematic people working in self-improvement today. He earned a PhD in applied math, founded a machine-learning hedge fund, and then built Clearer Thinking, a research organization that has run more than 75 studies and created over 80 free psychological tools used by hundreds of thousands of people. Through a project called Transparent Replications, his team also replicates studies from top psychology journals (chosen by a random number generator) to test whether the findings actually hold up.

His co-author, Jeremy Stevenson, also has a PhD and is a clinical psychologist who has treated hundreds of clients across thousands of therapy sessions at his practice.

Inside The Research Process That Simplified The Greatest Of All Time Self-Help Advice

Spencer and Jeremy set out to find every useful self-improvement technique in existence.

They started with the books that shaped the culture, like How to Win Friends and Influence People (1936), The 7 Habits, and Atomic Habits. Then they pushed outward into the obscure corners of the genre. They worked through 23 schools of therapy starting with the well-known ones: first CBT, the most validated therapy in the world; then narrative therapy and coherence therapy; and finally, methods so obscure that most therapists haven’t heard of them, hunting for techniques that had never escaped clinical training manuals.

Next, every time an author or therapist said “do this,” they extracted the technique into a spreadsheet. They purposefully ignored stories and philosophies. At 459 techniques, they hit saturation: nothing new was appearing.

When they sorted the pile, everything collapsed into 12 moves. Twelve underlying strategies, repackaged over and over with new names, new stories, and new branding. Every morning routine, therapy acronym, and productivity protocol you’ve ever tried maps onto one of them.

Spencer and Jeremy call them levers: the broad strategies for improving your life, each containing a set of specific techniques. (Think of “lifting free weights” as a lever, and the bench press as one technique inside it.)

If you’ve ever felt like you’re drowning in advice, this is your way out: the toolkit is finite. You don’t have to keep up with the infinite scroll of self-improvement. You can just learn all twelve moves.

Over 78 minutes, Spencer and I got into…

• Why 40 to 50 percent of papers in top psychology journals failed to replicate.

• The one lever Spencer would tell you to pull first.

• The most proven cure for insomnia, which almost nobody has heard of.

• How scheduling “worry time” helps you worry less.

• The failure mode of AI advice that Spencer named “light gassing” (it’s the opposite of gaslighting, and if you ever ask AI about your relationships, you need to know it).

• Why the placebo effect is real but much weaker than you’ve been told.

• What 3,000+ survey submissions revealed about intrinsic values, and how chasing the wrong ones can quietly cost you a decade.

• How invisible loops freeze our growth.

• The four categories of things you can actually change: your body, your thoughts, your attention, and your communication.

• How to become better at thinking.

• Spencer’s personal rule: always have one or two self-experiments running.

Take Action

Step #1: Watch the interview (free)

• The interview is 78 minutes

• Get interview resources (interview transcript, links to learn more, AI timestamps, AI chapter summaries)

Step #2: Get the book

The 12 Levers is out now in hardcover, ebook, and audio: grab it on Amazon, Bookshop.org, Barnes & Noble, or anywhere books are sold.

Step #3: Get the 12 Levers skill

The hardest part of the book isn’t understanding the levers. It’s knowing which one to pull when you’re standing in the middle of your own problem. We didn't want you to just watch this interview and move on, so we built a Claude skill that puts Spencer and Jeremy's framework to work inside your AI.

When you bring the 12 Levers skill to a problem, it runs the book’s diagnostic process to figure out what kind of problem you’re actually facing. A recurring loop? A values or direction question? A goal you can name but can’t reach? Then it:

• Recommends the lever that fits, by number and name, so you learn the framework’s vocabulary as you go

• Offers you 2 or 3 of the book’s top techniques for pulling that lever and lets you pick the one that feels most doable, because the repertoire is the point

• Tells you the honest evidence rating for each technique, using the book’s own labels

• Warns you about that lever’s failure mode (like exposure quietly undermined by safety behaviors, or reframing that slides into forced positive thinking)

• Coaches you on one small first action you can take today

• Points you to the chapter that teaches you the lever in depth, plus the matching free tool at clearerthinking.org where one exists

It’s good for questions like:

• Why do I keep having the same fight with my partner?

• How do I stop doomscrolling at midnight?

• I can’t decide whether to take this job.

• I don’t know what I want anymore.

• How can I be more present with my kids?

• Anything where you’d normally reach for a self-help book but not know which one.

If you run the 12 Levers skill in Claude Code or Cowork, it also keeps a private “lever log” on your own machine, tracking which levers you’ve pulled, which techniques you’ve chosen in the past, what you said helped, and which levers you haven’t tried yet, so you can see your patterns over time.

Two honest notes, because honesty is this framework’s signature:

First, the skill is a companion to the book, not a substitute for it. Every session points you back to a chapter, and the chapter teaches you the lever well enough to pull it yourself for the rest of your life.

Second, this is self-help, not therapy. The authors are clear that these tools can complement professional treatment but shouldn’t replace it, and the skill says so when it matters.

There are two ways to get the skill:

1. Become a paid member of this newsletter. Paid members can simply scroll to the bottom of this post for the download and install instructions. You can subscribe here.
   

2. Buy the 12 Levers book. Buy the book in any format by August 10. Then email a screenshot of your receipt to support@thoughtleader.school, and we’ll send you the skill.

Interview Resources

1. Interview Transcript

2. Learn More

3. AI Timestamps

4. AI Chapter Summaries

Interview Transcript

Read Transcript

Learn More

• The 12 Levers by Spencer Greenberg and Jeremy Stevenson

• 12LeversBook.com

• ClearerThinking.org

AI Timestamps

1. Spencer’s Path From Math to Psychology — 0:00–0:03

2. The Replication Crisis and Building The 12 Levers — 0:03–0:08

3. Lever One: Unraveling Root Causes and the ABC Method — 0:08–0:13

4. Self-Help Books vs. AI, and Shifting Trends in Self-Improvement — 0:13–0:17

5. The Truth About the Placebo Effect — 0:17–0:21

6. Self-Help, Rising Mental Health Struggles, and Evolutionary Mismatch — 0:21–0:24

7. AI’s Impact on Self-Improvement, Coaching, and “Light Gassing” — 0:24–0:31

8. Preparing for the AI Wave: The Overconfident Intern — 0:31–0:36

9. Psychedelics, Emerging Therapies, and the Need for Caution — 0:36–0:40

10. The Body-Mind Connection: Breathing, Exercise, and Sleep Restriction — 0:40–0:45

11. How to Use the Book: Root Causes and Intrinsic Values — 0:45–0:50

12. AI, Meaning, and Work in a Post-Job World — 0:50–0:55

13. Who Should Read The 12 Levers, and the Book’s Bonuses — 0:55–0:57

14. Worry Time: Taming Chronic Worry — 0:57–1:00

15. Feedback Loops: Why Psychological Struggles Get Stuck — 1:00–1:05

16. The Four Things You Actually Control — 1:05–1:11

17. Personal Experimentation and Spencer’s Self-Experiments — 1:11–1:17

18. Closing Thoughts — 1:17–1:18

AI Chapter Summaries

1. Spencer’s Path From Math to Psychology — 0:00–0:03

Michael opens the interview by welcoming Spencer and asking him to trace the path that led to his new book. Spencer explains that after a math PhD at NYU and building a machine learning hedge fund, he was looking for an application area for his technical skills and landed on psychology around 15 years ago, drawn to the opportunity to bring a mathematical, rigorous lens to a field he saw as full of room for improvement.

2. The Replication Crisis and Building The 12 Levers — 0:03–0:08

Spencer describes psychology as a much earlier-stage science than fields like physics, citing the replication crisis, where a large share of top-journal findings from 20 years ago failed to replicate. He discusses his Transparent Replications project, which randomly selects recent papers and attempts to redo their studies, and introduces “importance hacking”: a subtler, newer problem where legitimate results are made to seem far more significant than they actually are, fooling both readers and peer reviewers.

3. Lever One: Unraveling Root Causes and the ABC Method — 0:08–0:13

Asked for the single most valuable technique from the 12 levers, Spencer points to “Unravel the Causes,” illustrated with a car-repair metaphor: without a real diagnosis, people randomly swap parts instead of fixing the actual problem. He introduces the ABC method (Activating event, Beliefs/thoughts, Consequences) as a simple diagnostic tool, and shares the story of a woman whose seemingly psychological symptoms turned out to be carbon monoxide poisoning — a reminder to consider biological causes too. He notes the ABC method traces back to early cognitive-behavioral therapy.

4. Self-Help Books vs. AI, and Shifting Trends in Self-Improvement — 0:13–0:17

The conversation turns to why Spencer and his co-author focused on self-help books rather than only academic research, since that’s where most real-world techniques originate. Spencer argues that a well-crafted book is itself a highly designed intervention, which is different from what an AI can summarize on demand, so books retain value even as AI becomes more capable. He also traces how self-help trends rise and fall with cultural moods, criticizing a common “I fixed myself with one trick, so can you” genre as an unhealthy, overpromising pattern.

5. The Truth About the Placebo Effect — 0:17–0:21

Spencer explains that the placebo effect is real but often works best in self-fulfilling situations, like performance anxiety. He argues the effect is widely overestimated because studies conflate it with other factors such as regression to the mean — conditions like the common cold improve on their own, regardless of any placebo. Personally, Spencer avoids leaning on placebo-like self-deception because he deeply values truth and finds manufactured self-belief uncomfortable, even though he acknowledges it can genuinely help others.

6. Self-Help, Rising Mental Health Struggles, and Evolutionary Mismatch — 0:21–0:24

Michael asks how rising self-help content squares with rising rates of anxiety and depression. Spencer calls self-improvement a “mixed bag” — genuinely useful in parts, low-value in others — and argues humans weren’t evolved for happiness but for survival, with happiness merely one motivational tool among many. He suggests that modern life (constant screens, short-form video) has pushed us further from our evolutionary environment, compounding psychological strain in ways that are hard to isolate from any single cause.

7. AI’s Impact on Self-Improvement, Coaching, and “Light Gassing” — 0:24–0:31

Michael connects this to his own research into technology’s side effects, and Spencer predicts AI will be a much bigger psychological shockwave than social media, as people increasingly turn to AI instead of friends for advice. He coins the term “light gassing” — the opposite of gaslighting — where AI validates a person’s one-sided, distorted view of a situation because it’s optimized to please users. He also expects AI to disrupt the coaching industry, and describes launching an AI bot to help readers apply the 12 levers, framing the goal as producing more high-quality work with AI rather than more low-quality output.

8. Preparing for the AI Wave: The Overconfident Intern — 0:31–0:36

Extending the marathon-training metaphor Michael raises, Spencer describes learning to use AI well as an ongoing process of experimentation: neither dismissing it nor blindly trusting it, but treating it like a brilliant, overconfident intern whose reliability you learn area by area. He shares an example of using Claude Code, where the AI produced badly designed but functional code that would have gone unnoticed without careful review, underscoring that human judgment on process — not just output — remains essential. He also shares his own trick of instructing AI to always present the strongest arguments for and against unsettled claims.

9. Psychedelics, Emerging Therapies, and the Need for Caution — 0:36–0:40

Michael raises newer therapies like psychedelics and somatic approaches. Spencer notes psychedelics fall outside his book’s psychological focus but discusses promising early evidence for MDMA and ketamine, cautioning that small, early studies tend to overestimate effect sizes and that risks are often underestimated in the excitement phase. He draws a parallel to how SSRI antidepressant side effects, like sexual dysfunction in 40–50% of users, were underappreciated early on.

10. The Body-Mind Connection: Breathing, Exercise, and Sleep Restriction — 0:40–0:45

Spencer explains how the body and mind are deeply intertwined, giving breathing as an example: slower, exhale-emphasized breathing calms, while faster, inhale-emphasized breathing energizes. On exercise, his top advice is simply finding a form you enjoy, or using “temptation bundling” to pair it with something pleasurable. On sleep, he introduces sleep restriction, a paradoxical but well-proven insomnia technique where you shorten time in bed to improve sleep efficiency before gradually extending it.

11. How to Use the Book: Root Causes and Intrinsic Values — 0:45–0:50

Spencer explains why the book begins with unraveling root causes and then moves to intrinsic values: the things people care about purely for their own sake, not as a means to something else. Drawing on research with thousands of survey submissions and existing philosophical and psychological literature, he and his co-author mapped these into 22 categories. Michael connects this to his own experience at NYU business school, where most students defaulted into finance careers without examining what they actually valued.

12. AI, Meaning, and Work in a Post-Job World — 0:50–0:55

The discussion turns to how people might find meaning if AI displaces much traditional work. Spencer argues some intrinsic values, like connection or creativity, could still be met or even enhanced in a world of material abundance, while values tied to changing the world through personal action could become harder to satisfy as AI takes on more of that role. He voices cautious optimism, conditional on society successfully solving collective-action problems like AI safety and resource redistribution.

13. Who Should Read The 12 Levers, and the Book’s Bonuses — 0:55–0:57

Asked for his pitch to a skeptical reader, Spencer says he wrote the book he wished he’d had, distilling the small fraction of self-help material that actually works for both people burned by empty self-help promises and avid readers who think they’ve already seen it all. He outlines the three reader bonuses available at 12leversbook.com: a full reading list of source material, an AI bot to help apply the techniques, and a tool for building the techniques into daily habits.

14. Worry Time: Taming Chronic Worry — 0:57–1:00

Spencer shares a favorite underused technique, “worry time,” in which people write down each new worry once per day instead of dwelling on it, then set aside a dedicated time later to worry as intensely as they want about everything on the list. This reveals people have more control over worry than they assume, that a scheduled worry session often burns out faster than expected, and that it opens space for turning worries into concrete next actions instead of endless rumination.

15. Feedback Loops: Why Psychological Struggles Get Stuck — 1:00–1:05

Building on the sleep and worry examples, Spencer generalizes that most psychological difficulty comes not from the triggering event itself but from an unresolved feedback loop, as with PTSD, where prolonged exposure therapy works by letting a looping reaction finally resolve. He also describes how coping strategies learned in childhood, like acting out to get attention, can persist into adulthood because they still produce short-term rewards even as they cause long-term relationship damage.

16. The Four Things You Actually Control — 1:05–1:11

Spencer distills a core insight from writing the book: people ultimately control only four things — their body, their thoughts (specifically, their response to an initial thought), their communication, and their attention. Recognizing how little is actually controllable, he argues, reduces the suffering that comes from fighting uncontrollable things, and he offers a simple lens for tackling any challenge across those four areas. He adds that once basic physical needs are met, nearly all remaining suffering originates from thoughts, a realization he finds genuinely striking.

17. Personal Experimentation and Spencer’s Self-Experiments — 1:11–1:17

Spencer describes his habit of always running one or two lightweight self-experiments, tracked with simple daily notepad entries rather than rigorous data collection. He explains why he abandoned an earlier, highly randomized approach to self-experimentation: with only an “n of 1,” small effects (around 5%) get lost in day-to-day noise, so he now only looks for effects large enough to notice within a few weeks. He shares two examples — sleep-specific in-ear headphones that stay in place and play low-stimulation audio, and an ongoing experiment combining every sleep-onset technique he knows into one method.

18. Closing Thoughts — 1:17–1:18

Michael closes by praising the book and noting how the final segment shows Spencer isn’t just theorizing but actively experimenting on himself, dubbing Spencer’s approach “thought hacking.” He directs listeners to 12leversbook.com for the book’s bonuses and clearerthinking.org for Spencer’s free tools and newsletter, and the two exchange thanks.

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AI is one of the most important technologies ever created, not just for automating work, but for augmenting who we are. Yet most of what's out there teaches the software while ignoring the person using it.

In this session of Augmented Awakening, Anand Rao and I tried to close that gap from both sides at once: my side, the technical fluency; his, the emotional and developmental work of actually getting unstuck.

What came out of it wasn't the session either of us had planned twenty minutes before we went live… and I think that's exactly why it worked.

Anand didn't just tell us what's possible with AI now, he showed it with a self-made movie, a book written overnight, a working app built in twenty minutes. Then he handed everyone two new skills, /help and /unstuck, designed to make sure no one ever has to sit frozen in front of a blank chat window again.

If you've ever simultaneously felt the pull of what AI could do for you and the quiet resistance of "yeah, but not for me", this is the replay to watch.

During the class, we:

• Opened on the emotional and technical gap around adopting AI

• Anand previewed his upcoming Freedom to Be Yourself program

• Set today’s theme: agency, embodiment, and limitless creation

• Anand showed a playful AI-made pirate movie of himself

• Unpacked how we get “stuck” in unhelpful emotional states

• Introduced two new skills: /help and /unstuck

• Shared writing a 50,000-word book overnight with AI

• Live-built a travel planner app for coordinating time together

• Built a spinning, cross-sectional 3D heart model in minutes

• Guided a no-limits creativity brainstorm, then live-demoed /help

Implications

The real subject of this session wasn’t any single tool. It was the belief that sits underneath tool use. Anand’s phrase for it was simple: if you can think it, you can make it, at least in a first iteration.

That’s a bigger claim than it sounds. Most of us are still operating on assumptions about how long things take and how hard they are that were formed before this technology existed. And those assumptions, not our actual skill level, are usually what keeps us stuck.

The /help and /unstuck skills exist to interrupt that moment of paralysis directly, whether it’s a knowledge gap or an emotional one, and hand you the next smallest step you can take toward your goal instead of letting you close the tab.

The broader implication is one I keep coming back to across every program we run here: the tools are no longer the bottleneck. Agency is.

As AI keeps compressing the distance between an idea and a working version of it, the people who pull ahead won’t be the ones with the most technical knowledge. They’ll be the ones who’ve done the work to actually believe they’re allowed to try.

AI-Generated Podcast Summary Of The Class

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I’ve spent 10 years creating the most comprehensive encyclopedia of mental models ever. It includes 2,000+ mental models drawn from every major discipline and industry relevant to career success, business success, investing, personal growth, parenting, health, and relationships.

That wasn’t the real challenge though.

No matter how many mental models I gathered, applying the right combination of them in the moment remained elusive. The brain can’t recall 2,000 mental models, pick the 5-10 most relevant ones, and then fire them off in sequence. All in just a few seconds. That would take hours, which makes the entire enterprise impractical.

Thus, most people, even those who’ve studied mental models, still end up in a cognitive rut, using the same handful of models over and over.

For all of human history, we’ve had to settle for two modes of thinking:

• System I (fast, automatic)

• System II (slow, deliberate)

What’s different about this moment is that AI can fill in the gaps where the human brain is limited. In so doing, it can magnify our cognitive abilities, effectively increasing our IQ. This human + AI thinking system marks the dawn of System III thinking, which is both fast and deliberate.

I’ve spent the last year exploring how to deploy AI to this end. The end result was an AI skill that fires every time you use AI. This skill makes your AI smarter while teaching you more mental models.

I shared version 1 of the skill in February. It got more positive feedback than any prompt or skill I’ve ever shared before. So, I decided to create a version 2 of it. That’s what this monster article is about.

What You Get Today

Free Subscribers

The top lessons I’ve learned on my 10-year mental model journey, compressed into 10,000 words.

It includes:

• How version 2 of the AI skill works

• The key milestones along my personal journey

• A survey of the most important academic studies on mental models ever done

• A survey of some of the most important cognitive harness case studies

• A survey of how AI pioneers are using mental models to make smarter decisions

• How Human+AI thinking systems completely change the potential of cognition

Paid Subscribers

Version 2 of the mental model skill, which includes:

• A version for AI chat, and a version for agentic AI

• The full 2,000+ mental model encyclopedia

• An encyclopedia of 400 paradigms

• Step-by-step install instructions, with screenshots

• A six-step router: stuckness diagnosis → worldview identification → 338 rival worldview pairings → question de-biasing → 61 thinking recipes → self-verification

In addition to this skill, you get $2,000+ in other paid perks (books, skills, classes, mental model manuals).

Subscribe now

FULL ARTICLE

The first time I was told I wasn’t smart, I was eight years old.

I had missed the cutoff for my school’s gifted and talented program by a single IQ point.

At the time, all I knew was this:

• IQ is a fixed quality that you can’t change.

• Your IQ predicts much of your career performance.

• My two best friends got into the program, and I didn’t.

So there I was faced with a horrible feeling of inadequacy along with two truths that shouldn’t be able to co-exist:

• The belief that I could do anything (given to me by an immigrant mom).

• The message from the outside world that I would always be limited.

That single number going into third grade felt like a life sentence.

Psychologists have a name for what a near-miss like this does to the human psyche. Research finds that falling just short of an important threshold stings sharper, and lingers longer, than missing by a wide margin. For me, that sting didn’t fade for decades. The “almost” turned into fuel: if I couldn’t be born smart, I would outwork everyone who was.

When I Was 16, I Found A Shortcut To Effectively Increase My IQ

The shortcut was reading.

At the time, my business partner, Cal Newport, and I had co-founded a web development company despite having no business experience and little web development experience.

To compensate for this, we took the first $1,000 of our earnings and bought tons of books.

It worked amazingly well!

For just $15, we could get somebody’s life lessons distilled in 200 pages. We started with Adobe Photoshop, HTML, business, typography, and color theory. And, the client ended up being very happy with what we created.

This learning shortcut turned into a multi-decade habit of ravenous reading. I still think that the information in books has the highest concentration of wisdom with the least distraction.

The next shortcut came in 2015 when…

I Found Out How To Exponentially Increase My Rate Of Learning

At the time, I had a thought that changed everything:

“If I’m going to read thousands of books in my life, why don’t I just deliberately learn how to read and learn faster and better?”

So, I took a step back and thought more deeply and systematically about intelligence amplification:

• I spent 100+ hours reviewing the most rigorous academic research on IQ intervention.

• I collected all the learning models I could find across different fields.

• I found research showing that diet and exercise interventions can have a profound impact on intelligence (see the appendix for the most fascinating research).

• I studied the learning habits of top entrepreneurs, investors, and executives.

As I was doing all of this, one thought was guiding me:

“Machines like the horse-drawn plough reduced the importance of physical strength in the world. What is the plough that augments the mind and reduces the relative importance of IQ?”

As an adult, this question felt significant to me. My logic was this:

• Intelligence is important across humans, animals, and systems.

• If intelligence were malleable, it would have profound implications not only for my own life but also for democratizing access to its benefits in society.

This period led me to several ways to learn faster. Below are a few of the simplest and most profound takeaways packaged into articles I wrote:

• Modern Polymath: People Who Have “Too Many Interests” Are More Likely To Be Successful According To Research (92,000 likes)

• The 5-Hour Rule: If you’re not spending 5 hours per week learning, you’re being irresponsible (68,000 likes)

• The Learning Loop: Memory & Learning Breakthrough: It Turns Out That The Ancients Were Right (2,600 likes)

• Fractal Reading: Augmented Reading: Learn 10x Faster And Better With AI

• The Explanation Effect: Memory & Learning Breakthrough—It Turns Out That The Ancients Were Right (10,000 likes)

• How Elon Musk Learns Faster And Better Than Everyone Else (21,000 likes)

Over time, my opinion on IQ as a fixed, all-important trait evolved, and I concluded...

Forget About High IQ. What Really Matters Is Augmented Intelligence.

My experiences and research have convinced me to adopt a growth mindset about intelligence. Many proven interventions increase or augment IQ. Not only that, the IQ of people in developed countries has increased dramatically in the 20th century, showing that it’s not a fixed variable...

At the same time, I learned that the line doesn’t just go one way. Over the last two decades, the trend has reversed itself:

Then, I came across a fascinating pattern...

The World’s Smartest Entrepreneurs And Investors Use Mental Models To Augment Their Intelligence

I learned about the power of mental models when I began studying the learning habits of many of the world’s most successful entrepreneurs, investors, and innovators, including Jeff Bezos, Elon Musk, Warren Buffett, Peter Thiel, Howard Marks, Ray Dalio, Drew Houston, and many more. Each had the peculiar habit of collecting and coining mental models.

Some went even further. For example, Charlie Munger, Warren Buffett’s lifelong business partner, built an entire operating system for making smart decisions with a “latticework of mental models.”

Here’s what he did:

• Identified that mental models are the most powerful unit of learning. These models are compact, reusable ideas that explain how important parts of the world work.

• Collected the big models from the big fields. Each field has unique insights. And not all models are created equal. Some are more important than others.

• Learned the most important 100 mental models. Enough to cover the most important models. He chose not to spend forever learning thousands of models.

• Applied each of the models religiously. Every time he made an investment decision, he used two checklists.

At a more profound level, Munger believed reality was one connected system. That while academia splits reality into separate disciplines, reality itself does not respect the boundaries between economics, biology, and psychology. Said differently, there are deep, fundamental principles that apply across fields that most people overlook, because those principles are not taught in any one specialty.

Elon Musk echoed the same sentiment:

“It is important to view knowledge as sort of a semantic tree — make sure you understand the fundamental principles (Musk calls these ‘first principles’), i.e. the trunk and big branches, before you get into the leaves/details or there is nothing for them to hang onto.”
—Elon Musk

I created the following visual to make sense of the quote:

Because knowledge is interconnected in a tree-like structure, mental models can often be used across many fields to generate big, unique insights and make smarter decisions.

Munger returned to the power of this idea at the 2007 USC Law commencement:

“There are all these other things that you should know in addition to history. And those other things are the big ideas in all the other disciplines. And it doesn’t help you just to know them enough so you can prattle them back on an exam and get an A. You have to learn these things in such a way that they’re in a mental latticework in your head and you automatically use them for the rest of your life.

If you do that, I solemnly promise you that one day you’ll be walking down the street and you’ll look to your right and left and you’ll think, ‘My heavenly days, I’m now one of the few most competent people of my whole age cohort.’ If you don’t do it, many of the brightest of you will live in the middle ranks or in the shallows.”

—Charlie Munger

Sit with the implications of what Munger is claiming, because they operate on three levels at once:

• The personal claim. The ceiling on your decisions is not your intelligence or your effort. It is the number of models you can actually reach for in the moment. Almost everyone reaches for the same three or four.

• The professional claim. Across many fields, the people who carry the best models from many disciplines beat the specialists who run everything through one. Research backs this claim. Consider the 20 most significant scientists in history, as ranked in Human Accomplishment, which scored thinkers by how much space reference works devote to them. 15 of them were polymaths. Newton. Galileo. Aristotle. Kepler. Descartes. Huygens. Laplace. Faraday. Pasteur. Ptolemy. Hooke. Leibniz. Euler. Darwin. Maxwell. All polymaths. Not only that, the founders of the largest companies in the world are nearly universally polymaths. Bill Gates, Steve Jobs, Warren Buffett, Larry Page, Elon Musk, Mark Zuckerberg, Jeff Bezos.

• The civilizational claim. The real limit on our breakthroughs has rarely been how much we knew. It’s been how few minds could combine what they knew across the walls between fields. That kind of mind, holding many disciplines in view at once, has been one of the rarest advantages in history, and building such a mind costs a lifetime of work.

Munger isn’t alone.

Many entrepreneurs have their own unique approach to collecting, creating, and chaining mental models...

Everyone Who Thinks For A Living Collects And Coins Mental Models

Elon Musk is famous for reasoning from first principles instead of by analogy, a concept that is common in physics:

Source: Kevin Rose Podcast

He’s also famous for using other models, such as thinking in the limit, probabilistic thinking, attacking the bottleneck, and his own 5-step iterative product development algorithm.

Ray Dalio, who built the largest hedge fund in the world, wrote 600 pages of Principles based on the same instinct:

“Principles are concepts that can be applied over and over again in similar circumstances, as distinct from narrow answers to specific questions. Those who understand more of them, and understand them well, know how to interact with the world more effectively than those who know fewer of them or know them less well.”
Ray Dalio, Principles

Dalio uses the word “principles” where Munger says “models,” but he is pointing at the same thing.

Brian Chesky, the co-founder and CEO of Airbnb, coined his own mental model, the 10-star experience, and built his company around it:

John and Patrick Collison, the founders of Stripe (≈$150B valuation), were so taken with Munger’s latticework that they republished his book, Poor Charlie’s Almanack, under their own publishing press.

The Nobel laureate Herbert Simon, one of the pioneers of artificial intelligence, put it plainly decades ago:

“The quality of our mental models determines how well we function in the natural world.”

The list of unique models people regularly use is surprisingly varied:

…and the list goes on.

Mental models were just as impactful in my own life…

How Mental Models Changed My Life

Explaining the massive shift that mental models made in my life, I wrote the following about my firsthand experience in Most People Think This Is A Smart Habit, But It’s Actually Brain-Damaging (2020):

I saw reality on a much deeper level — and on a fundamentally different level. I looked back on many of my old mistakes, and thought to myself, “Oh my God! If I had only known this or that mental model…” I wasn’t just learning new strategies or hacks.

On some level, I could relate to some of my favorite movie characters just after their intelligence had exploded…

First, I saved over $100,000 dollars…

For example, 12 years ago, I borrowed $100,000 from friends, vendors, and banks (at high interest rates) to keep a struggling website I created alive. Rather than facing obvious indicators that the idea wasn’t working, I kept on doubling down. I was in love with my idea, and I didn’t want to admit defeat. The company died a slow, painful death.

Now that I understand mental models, I see how one cognitive fallacy — “sunk cost fallacy” — caused my poor decision-making. This mental model has helped me discontinue failing ideas much more quickly.

Today, when I consider new business ideas, instead of just imagining how great they’re going to be, I also envision what could go wrong — the inversion mental model — saving a lot of my time and money upfront. For example, a few months ago I had the idea to create a book summary of the month club where I would write a weekly in-depth summary of a life-changing book. I got really excited about it and spent 10 hours thinking about how great it was going to be. A few days of planning later, I decided to take a step back and honestly assess the downsides. Almost immediately, I started seeing some glaring roadblocks, and the new shiny object was no longer as exciting. Soon after, I decided to just focus on our core businesses. I’m very happy that we did. Twelve years ago, I would have jumped in straight away.

Also, our article virality shot through the roof…

The success of our business is directly related to the number of views each article gets. So, being able to get hundreds of thousands of visitors per month without paying for ads is a big deal.

After learning about the 80/20 Rule (which is now one of my favorite mental models), I started asking the most successful article writers what the 20% activities were that give them 80% of the results. Almost all of them mentioned that titles were key. Previously, I viewed the titles as just an afterthought.

Because of this insight, my team and I restructured our entire article creation process:

• Now, we create titles before we write articles.

• Rather than spending 5 minutes on titles per article, we dedicate 5–10 hours per article.

• For every article, we brainstorm over 30 titles rather than a handful.

• We test the article titles before we publish them.

Our team has now spent over 1,000 hours studying the patterns of titles and testing nearly 5,000 titles. As a result, we have a fundamentally different and better understanding of what makes articles go viral.

This is one of the mental models I use for my articles to be viewed tens of millions of times…with the average article now being viewed 150,000+ times.

Finally, I started making a lot more money.

• I have had people hire me for six-figure consulting contracts.

• I began charging $500 per hour as a coach and consultant.

• I even got a five-figure speaking gig from an article.

• When I launched a new company, we were able to create a business strategy based on “breakthrough knowledge” that is hard to replicate. This one strategy alone made us over 7-figures in revenue.

I once heard a coach talk about changing a client’s way of seeing the world in a way that would blow their mind. When he looked into his client’s eyes and could see him or her really getting it, he’d say, “Now, you’re in my reality!”

That’s how I felt.

Reality somehow feels different on an aesthetic level — as if I’m cutting through the levels of illusion and noise we normally see and getting a more direct view.

The best way I can describe this is that it’s like wearing augmented reality glasses that constantly feed you relevant wisdom about the situation you’re in.

Then, around 2021, I started to feel limited exploring mental models just through the lens of career success.

Mental Models Are More Than About Career Success. They Are About Life Success And Happiness.

Around 2020 and beyond, two things changed:

• Society changed. I realized that entrepreneurs that I had long-respected weren’t the best role models outside of business. I won’t name names, but you know who I’m talking about.

• I changed. As a parent of two children at the time and in a 20+ year marriage, although I loved business, it was no longer the center of my life like it was in my 20s. My family, quality of life, health, impact, and personal growth had taken center stage.

These two changes led me to explore mental models for:

• Relationships

• Health

• Emotional Regulation

• Developmental Growth

• Somatic Awareness

• Etc.

This had a profound impact on me. Here’s an example of one shift.

When I first became a parent at 26, I was flying blind. None of my friends had children yet, and the books about parenting for dads were woefully inadequate.

Then, last November, 17 years later, my wife and I had our third child—Naya:

That’s when I really saw the shift in myself and the power of mental models.

Here’s a concrete, recent example. With my first two children:

• Awareness. I was only focusing on major milestones (eg, walking, crawling, talking), and I ignored smaller, real-time milestones. This happened because my model of development was low resolution. This made it hard for me to see how much my kids were evolving every single day.

• Knowledge Transfer. Parenthood and business felt like completely different domains. It was hard to transfer commonalities between them. Therefore, it was hard for me to apply parenting lessons to life/business and vice versa.

• Parenting Mental Models. I wasn’t aware of mental models for emotional regulation techniques or somatic awareness. This made it hard for me to constructively process the most difficult parts of parenthood. Which led to overwhelm.

Now things are different on multiple levels:

• Awareness. Rather than focusing on big milestones, I’m able to notice the smallest ones. This makes the experience feel more present and alive.

• Knowledge Transfer. Now that I’m able to abstract experiences into deeper models, it’s easier for me to learn something in one area of my life and generalize it across other areas. Thus, each area of my life feels more integrated. Parenting feels related to every other area of my life, which makes it more engaging.

• Parenting Mental Models. I feel well-resourced as a parent because I have a whole toolkit of mental models that help me. Some come from the field of parenting. Others are transferred from other fields.

And now that AI exists, I’m able to take things to another level.

Given how deeply I’ve studied learning, growth, and developmental psychology, I have many mental models in that area.

Earlier in my daughter’s development, I noticed that she learned to intentionally let go of things she was holding on to. Normally, I would think nothing of it. But, I now see letting go as an important developmental milestone. And I’m able to relate to it by thinking about how it applies in other domains (eg, emotionally letting go) in my life. Seeing her learn to let go inspired me to do the same better in other areas of my life.

Seeing this pattern, I was then able to ask AI the following question:

“Many people notice milestones like standing, but they miss milestones like falling. Or they notice grabbing, but not letting what go. What are other milestones of development that often go overlooked? And how do those milestones mature as humans evolve, and how are they relevant to me now?”

This yielded a table with 68 developmental pairs. You can see the full list in the appendix, but here’s a sampling:

This table has shifted how I think about development on a deeper level and led to deeper conversations with AI.

My point is that when I was younger, I would’ve completely missed noticing almost all of my kid’s milestones. Now, I’m not only able to experience them in real-time with her, but I’m also able to participate in and learn from them, because I have a deep understanding of mental models and AI.

These shifts transform parenthood in the best ways. They deepen my relationship with her. They help me grow, and they help me help her grow. And they make parenthood more fun.

From Anecdotes to Evidence:

At this point, you could object. You could say that this list and my experience are survivorship bias:

• That successful people are just a small sample.

• That these models are just the story they tell about their luck.

Fair.

So set the anecdotes aside and look at what happens under controlled conditions...

Seven Studies On Why Range Beats Brilliance

Range is carrying many models from many disciplines, and reaching for the right one when it counts. Across seven studies and very different methods, the same finding recurs: expertise is built from models, and the breadth to deploy the right one beats raw brilliance:

1. Experts think about what mental models are relevant to a problem before trying to solve it. Amateurs don’t.

2. Experts with more, diverse mental models outperform experts with few models from one field.

3. The most creative experts have the widest range of interests.

4. Your range of outside interests predicts success better than your IQ.

5. Storage of mental models is not enough. Retrieval is key.

6. The basis of expertise is the number of stored mental models.

7. The pattern applies to teams as well.

These aren't anecdotal claims. Each one comes from research spanning four decades: a 20-year forecasting tournament tracking 28,000 predictions, creativity studies following scientists over 25 years, controlled experiments in analogical reasoning, and the original chess expertise research that helped launch cognitive science.

Together, they point to the same conclusion: the ceiling on your thinking is not just your raw intelligence. It’s the number of mental models you can store, retrieve, and fire at the right moment. If you want the full breakdown of each study, including the methods and specific results, I've included it in the appendix at the end of this article.

Bottom line

For decades, collecting mental models was the best advice anyone has given on how to think.

But, Charlie Munger is the one who turned it into an operating system.

To understand why what Munger did was so rare, you have to first look at the more ordinary things people do with models…

There Are 5 Levels Of Mental Model Mastery. Munger Was One Of A Few People To Reach Level 5.

There are five levels of engagement with mental models, each rarer than the last. Munger reached the fifth, and until very recently, very few people could follow him there.

• Level #1 - Using Mental Models Without Knowing It: Close your eyes right now. If I asked you to walk outside of the building you’re in right now, you could probably do this. The reason you can is that your mind unconsciously built a model of the building in your head that you can use to navigate physical space without sight. Your mind automatically builds all sorts of other models. Models of individual people, models of products, models of businesses, models of society.

• Level #2 - Using Models On Purpose: Not all models are helpful. Some are so outdated or overused that they do more harm than good. This is why using models consciously is so important. For example, you use one consciously the moment you catch yourself thinking ‘that’s a sunk cost’ before finishing a book you stopped enjoying 100 pages ago. You know the model by name, and you reach for it on purpose.

• Level #3 - Collecting the Best Models: The most successful thinkers and experts don’t just use models based on their life experience. They consciously collect and use the best ones of all-time. For example, the large list of successful model collectors I shared at the beginning of the article.

• Level #4 - Creating Your Own Models: Jeff Bezos didn’t borrow regret minimization, he coined it. Nassim Taleb gave us antifragility. Ray Dalio built believability-weighted decision-making. Annie Duke named resulting.

• Level #5 - Writing the Rules For Running Models: Munger did something no one had ever done before. He wrote down the rules for operating mental models.

Charlie Munger’s 5-Step Operating System For Thinking

A mental model is a compressed piece of how the world works, borrowed from a field that spent centuries figuring it out.

• Supply and demand from economics

• Compounding from mathematics

• Natural selection from biology

Each one is a small machine for prediction. You feed a situation in one end and a better guess comes out the other.

No single model is enough, because every field only sees its own slice of reality. Stack enough of them across enough disciplines and you stop seeing a narrow problem and start seeing the whole one.

Here is his operating system, in his own words:

Rule #1: Learn Multiple Models

The first rule is that you’ve got to have multiple models—because if you just have one or two that you’re using, the nature of human psychology is such that you’ll torture reality so that it fits your models.

It’s like the old saying, ‘To the man with only a hammer, every problem looks like a nail.’ But that’s a perfectly disastrous way to think and a perfectly disastrous way to operate in the world.

Rule #2: Learn Multiple Models From Multiple Disciplines

And the models have to come from multiple disciplines — because all the wisdom of the world is not to be found in one little academic department.

Rule #3: Focus On Big Ideas From The Big Disciplines (20% Of Models Create 80% Of The Results)

You may say, ‘My God, this is already getting way too tough.’ But, fortunately, it isn’t that tough — because 80 or 90 important models will carry about 90% of the freight in making you a worldly-wise person. And, of those, only a mere handful really carry very heavy freight.

Rule #4: Use A Checklist To Ensure You’re Factoring in the Right Models

Use a checklist to be sure you get all of the main models.

How can smart people be wrong? Well, the answer is that they don’t…take all the main models from psychology and use them as a checklist in reviewing outcomes in complex systems.

Rule #5: Create Multiple Checklists And Use The Right One For The Situation

You need a different checklist and different mental models for different companies. I can never make it easy by saying, ‘Here are three things.’ You have to drive yourself to ingrain it in your head for the rest of your life.

The lattice Munger described, dozens of ideas firing together and checked against each other, is wonderful in theory.

But over time, as I tried to implement it, I noticed diminishing returns: each new model I learned added less value than the one before...

6 Reasons The Latticework Ultimately Lets Down Every Human Who Tries It

I have been studying mental models for years, and I have never met a single person who actually runs the latticework the way Munger described it. Not one.

Here are the six reasons why:

1. Invisibility of our thinking. You cannot see the unconscious models already running your decisions, so you cannot correct them.

2. Limited time. You cannot learn and operationalize all the good ones unless thinking is your full-time job.

3. Forgetting models. Even the models you have learned rarely surface at the moment they would help, which is the whole reason Munger reached for written checklists.

4. Need for immediate decisions. A checklist works when you have time to deliberate. It does nothing in the middle of an argument or when a fast decision is required.

5. Limited working memory. Your working memory holds only about three or four things at once, so you can never actually run many models in parallel.

6. Emotions distorting thinking. Knowing a model is not the same as applying it when you’re emotionally triggered, and the bigger the decision, the more feeling floods in. You can recite sunk cost and still refuse to walk away from what you poured years into, or know the priority cold and still not bring yourself to say no.

In practice, almost everyone collapses back to the same three or four models, over and over.

And they’re not even choosing those three or four because they fit the problem. They reach for them because they are familiar, comfortable, already wired to fire. Picture the person who runs everything through incentives, supply and demand, and first principles, while the biologist’s lens, the historian’s, and the negotiator’s sit unused on the shelf, even when one of them would have cracked the problem wide open.

Seeing a situation through a model the way those experts do is not a matter of reading a definition once. It takes dozens of hours to learn a single model well enough to fire it automatically wherever it applies. That is the price of a single model. And the number you need is far larger than anyone has admitted.

Munger thought 80 or 90 would cover it. The models genuinely worth having run into the thousands. Do the arithmetic: thousands of models at dozens of hours each is more time than any lifetime holds.

I should know.

I spent five years cataloging them, my encyclopedia is past 2,000, and I am nowhere near done. Everyone who chases the latticework, including the people who admire it most, learns a sliver, and truly operationalizes a sliver of that sliver.

Researcher on polymathy Dean Keith Simonton puts the challenge well:

“Someone can’t just say, “Well, as of today I’ll have extremely broad interests so that tomorrow I’ll be a creative genius.” Doesn’t work that way. The broad interests are involved in expanding a person’s knowledge base, and that takes considerable time.”
—Dean Keith Simonton

I had a head start most people never get. For four years, I published a 10,000-word manual on one mental model every month. I built an encyclopedia. And still, on a normal Tuesday, with a real decision in front of me and no time to deliberate, I reached for my same few favorites. I was using more models than almost anyone I knew, and I still felt like I was living at 1% of what the latticework promised.

When I talked to mental model students about this, the story got worse, not better. They saw the value immediately. Then they saw the work, the years of study, the thousands of models, the checklists you build and rebuild, and they quietly decided it is not for them. They were not wrong about the cost. For a human, the cost is the whole problem.

So the latticework was always two things at once:

1. It was the most powerful idea anyone has had about how to think.

2. And it was almost completely out of reach for the average person.

Years of work bought you a sliver, and that sliver could never run at full bandwidth. The dream of mental models was a mind deep enough to hold a hundred models, fast enough to fire the right one on cue, and wide enough to run hundreds at once. No human ever had it.

It’s as if mental models are this incredibly powerful intelligence enhancer, but the human brain simply isn’t designed to run them at their max. It’s like receiving a technology from the future, but not having the right power source to make it run.

Bottom line:

While mental models are extremely powerful, it takes thousands of hours to truly master them. And even then, it’s simply hard for the human brain to use multiple mental models at once in real-time in all of the relevant situations in our life where they could make a profound difference

Then I added one sentence to the end of a question I asked AI, and the limit I had treated as a law of nature turned out to be surpassable...

One Sentence Brought Every Limit Down

Here is the thing that took me embarrassingly long to see. I had spent five years treating the six-limit ceiling as fundamental. It is not. Rather, it is a fact about how human thinking works. Think with AI, and the ceiling moves.

The sentence I added was, more or less, this:

“Break this down to first principles, then find the incentive everyone is ignoring, then run it through the lens of a biologist, then steelman the opposite of whatever you just concluded, then tell me what a historian would notice.”

The first time I did it, the answer was not a little better. It was a different kind of answer. And, it came back in seconds.

This made me realize that I could almost treat mental models as a command language and use AI to process multiple mental models in sequence.

To put the power of having a command language in context, I share the following in Vibe Prompting Method:

“Every field has this hidden vocabulary—a minimal set of high-leverage terms that compress expertise into commands. Musicians have it. Coders have it. Designers, writers, analysts—all fields have it.”

And once you figure out that command language, you can do incredible things. For example, in this 2-minute video, a musician uses words I’ve never heard before to compose an amazing beat:

In another video, we see famous movie director Martin Scorsese immediately take a scene and use his expertise to command AI in a way that a novice never could.

What I now see is that mental models are the command language for thinking. Point models at AI, and you can make it smarter.

This experience begged a question:

What if AI could operate Munger’s system?

What I now believe is that the six limits I mentioned earlier are human limits. Not one of them belongs to AI:

• AI thinking is visible. You cannot see the models running in your own subconscious. With AI, you name a model, and it applies exactly that one, on command, and you can read the text of exactly how the AI is applying it.

• AI already knows the encyclopedia. You cannot learn and operationalize them all in a lifetime. AI already has. Every framework I spent years writing up is sitting inside it, fully operational, for the price of a monthly subscription.

• AI doesn’t forget. The right model rarely surfaces when you need it. The machine forgets nothing. Its whole library can be loaded, so the model that fits is always there, no checklist required.

• AI thinks in seconds. A checklist is useless in the heat of the moment. The machine fires the right model the instant you name it, with no warm-up and no fumbling.

• AI doesn’t have the same limited working memory. You can hold only about three or four models in your head at once. The machine holds 750,000 words in its working memory. Ask it to run 10 in sequence on one decision, then 10 more, and it does not get tired, and it does not drop the thread.

• AI doesn’t have emotions. You cannot apply a model when the feeling is running the show. The machine has none. It applies sunk cost to the project you cannot bear to kill and says no to the request you cannot bring yourself to decline, because it is not the one who has to feel it. This helps reveal thoughts your emotions would hide.

Next, I decided to move to a higher leverage approach...

A Few Paragraphs That Upgrade Every Answer By Default

I stopped retyping the instruction onto every question, and I wrote a system prompt.

A system prompt is just the block of instructions a model reads before it reads anything you type. Both Claude and ChatGPT allow you to create one in the settings pages of each. So instead of adding that one instruction onto the end of every question by hand, I wrote it in the settings pages once. Now it runs on every conversation, whether I remember to ask for it or not. I no longer have to decide, question by question, whether to think this way. It happens on its own.

Here is what it does. You ask it anything, and instead of one answer, you get four:

#1. plain answer

This is the response the model would have given on its own, untouched, before any of this fired. I keep it on purpose. It is the control, and the whole point is to see what the rest of it adds.

#2. It shares my cognitive signature

It reads my own thinking back to me based on how I framed the question. It shows me the following thinking I used:

• Frame(s)

• Paradigm(s)

• Mental Model(s)

Then, it shares frames, paradigms, and mental models that were in my blindspot, and it reveals what they would’ve shown me if I had asked the question with these.

All in all, this shows me the assumptions I am working from.

#3. It shows its work (reasoning trace)

It names the sequence it is about to run and walks through it a step at a time: this move, through this lens, and here is what that step turned up. I get to watch the thinking happen instead of being handed a verdict, which is the only way I have ever actually learned a new move.

#4. It provides a model-enhanced answer

This is the one that sequence produces. Not a cleaner version of the first answer. A different kind of answer.

The power of this system prompt is that it provides me with better answers, and it helps me improve my own thinking.

Watch what that looked like on a sample question

The easiest way to show you is to walk through a real one:

How can I 100x the speed and quality of my own learning?

You can get the link to the full answers in the appendix. Below is a summary of each response:

Plain answer: The plain answer came back with a genuinely good list. Six solid pieces of advice, ranked from most to least useful. To summarize them:

1. Compress your feedback loops

2. Predict before you read

3. Hunt for disconfirming evidence

4. Teach in public

5. Diversify your inputs

6. Use AI as a sparring partner instead of an oracle

If I’d stopped there, I’d have been happy. It’s the kind of answer most people would be thrilled to get.

Reasoning Trace: Then I watched it think. This is the part worth slowing down for, because you can see the actual mental models it reaches for.

First, it used first principles: stripping a question down to what’s actually true at the bottom, underneath the usual assumptions. It pulled “learning” apart into the separate jobs hiding inside it:

• Information intake

• Pattern recognition

• Conceptual integration

• Skill acquisition (procedural)

• Identity/perspective transformation (developmental)

• Embodied integration (somatic)

• Retrieval and application

• Calibration / error correction

Then it ran those through the 80/20 rule: the idea that a small handful of things produce most of the results, and asked which single one would matter more than all the rest combined. The answer caught my attention: not taking in information, the thing I spend most of my time on, but how fast and how often you catch yourself being wrong and fix it.

Next, it questioned the hidden assumptions in my question. (Philosophers have a name for this: Cartesian doubt, deliberately distrusting everything you’ve been taking for granted.):

• That “learning” is a single variable.

• That faster is better.

• That the rate-limiter is me.

Then it used analogical reasoning: borrowing a solution from a completely different field that’s already cracked the same kind of problem. Who else learns incredibly fast, and what do they actually do? It focused on:

• Chess players who train against a computer that grades every move the instant they make it.

• Surgeons who walk back through exactly what went wrong after a patient dies.

• Special-forces teams that pick apart every mission the moment it ends.

• High-frequency traders who deploy tight learning loops.

• Bayesian forecasters that track every prediction and calibrate over time.

And it stopped itself on the one thing they all share: none of them got faster by taking in more. Every single one got faster by shrinking the gap between making a guess and finding out whether the guess was wrong.

It kept going, a Dunning-Kruger check, an adult-development read, a probe at the edges of the question, before it turned on itself.

Finally it tried to prove itself wrong: the move scientists call falsification, and the fastest way to find the holes in your own thinking. If all of this were right, it reasoned, then for someone like me, dropping things should help more than adding them. I heard that as a quiet accusation: I’d been making a fancier version of a beginner’s mistake, piling up better tools, which feels exactly like progress, instead of changing the one thing that moves the needle.

Enhanced Answer: Then came the answer all that thinking built, and it looked nothing like the list I started with.

It didn’t give me a better list at all. It told me, gently, that I’d asked the wrong question. You can’t make your learning a hundred times faster, it said, because “learning” is too small a thing to multiply like that in the first place. The thing that actually multiplies is the whole setup your learning happens inside.

Fix the setup, and faster learning just falls out of it. Then it gave me a metaphor that drove the point home: trying to learn a hundred times faster is like trying to breathe a hundred times faster. You can’t, and you wouldn’t want to. What you do instead is change the air, build bigger lungs, and let your body quietly start pulling more out of every single breath.

Then it told me three things it recommended changing along with personalized advice on how to make the changes:

1. Change how fast I find out when I’m wrong.

2. Use my body to turn thinking into instinct.

3. Let go of my own ideas, including the ones I’m proudest of, the ones I’ve built my work on.

Bottom line:

Two answers, side by side on the same screen:

• The sensible list I started with.

• The complete rethinking of the question I was even asking.

It was the same question and the same AI, but mental models completely changed the response.

For Months, I Didn’t Believe My Own Results, But Then The Feedback Started Pouring In

It almost felt too good to be true. I wondered if I was just imagining the improvement. So, I shared an upgraded version of the prompt that tapped into mental models in a more organized way in an article: This “AI Command Language” Upgrades Claude to Opus 5.6.

After publishing the article, the feedback started trickling in. I got more people sharing the impact than almost any other post I’ve shared.

More so, when I talked to people one-on-one, multiple high-level entrepreneurs and executives said they use the skill I created every day and found it extremely helpful (and even life-changing). Here are a few samples:

Joe Stolte, 5-Time Founder With Three Exits

Sean Cushing, Co-Founder of Cantina Creative (clients: Avatar, Atlas, Aquaman, The Marvels, Black Adam, Thor, Captain Marvel, Avengers, The Hunger Games, Guardians Of The Galaxy)

Abhinav Sarapure, Lead Business Intelligence Engineer, HubSpot

Seeing these results, my curiosity was piqued. I wondered...

What are the limits of how smart AI can be with the right “thinking workflow / cognitive harness”?

That’s when I began a new line of research...

The Fascinating Research Behind Cognitive Harnesses

That’s when I saw a lineage of people using a few sentences of scaffolding to help models reason better, and the results have been pretty astounding:

• Simply adding the words “let’s think step by step” lifted an older model’s score on a set of math word problems from 17.7% to 78.7%.

• Letting the model try several paths and back out of the dead ends instead of marching down one line took GPT-4 on the Game of 24 puzzle from 4% to 74%.

• Handing it a small kit of named reasoning moves to run on demand lifted a standard model from 32% to 53% on a hard math benchmark, past a specialized reasoning model that costs far more.

• Giving a model tools instead of more size let a 6.7-billion-parameter model match models roughly 25 times larger once it could reach for a calculator and a search engine.

• Microsoft Research published a method, SkillOpt, that improves an AI by training the instruction document you hand it rather than the model itself. Freeze the model, optimize the words. It was best or tied-best on all 52 of its tests.

More recently, companies have been moving beyond adding a few words and building entire thinking cognitive workflows on top of AI. The first to really capture my attention is a company called Poetiq. They call what they built a “cognitive harness,” and the term fits the whole category, so it’s the one I’ll use from here.

Here’s what a cognitive harness did for Poetiq.

Poetiq, a six-person team of ex-DeepMind researchers, built a cognitive harness that beat the top AI models on one of the hardest benchmarks in the field. They took the standard Gemini 3 Pro model (not Google’s far more expensive Deep Think reasoning mode) and wrapped it in their cognitive harness. Then they ran it against Deep Think on the very well-respected ARC-AGI-2 benchmark. This benchmark is a set of abstract visual puzzles designed to be simple for a person and punishing for a machine. The average human scores around 66%, while the top models were stuck in the single digits when it launched. It rewards figuring out a brand-new pattern on the spot, not recalling something from training.

Deep Think had just set the top of the leaderboard at 45%. Then, Poetiq scored 54%, verified by the ARC Prize team, at less than half the cost per problem ($31 versus $77).

Google pours billions into training that model and employs some of the most decorated researchers in the field. Poetiq is six people. No custom training data. No hundred-million-dollar compute budget. They didn’t build a better brain. They built better thinking on top of a brain.

That last line is worth slowing down on. A cognitive harness is a layer of structured thinking that sits on top of an AI model and organizes how it reasons: which cognitive moves to make, in what order, and how to check its own work. The model underneath never changes. It’s the same Gemini or Claude everyone else types into. The harness is the judgment you wrap around it, so the machine works the problem the way an expert would.

Source: Poetiq

The interview clip with co-founder and CEO Ian Fischer sealed the deal for me:

Source: Y Combinator

Even then, I hesitated to build a cognitive harness, because so many of its components are copyable that could get absorbed into the next model. But the deeper I went, the more convinced I became that the part that compounds, the opinionated judgment you encode, is the part the labs can never absorb. Fischer explained it best.

The conventional approach to making AI smarter in your domain is fine-tuning: you collect a massive dataset, spend months and millions training the model on it, and end up with something that performs a bit better on your specific problem. Then the next model comes out. And it’s better than your fine-tuned version out of the box. As Fischer put it:

“You’re going to spend a lot of money on that fine-tuning. The compute is so expensive. And then at the end of it, you have something that works better than the thing that you fine-tuned on top of, but by then, a new model’s come out, and it’s better than the thing that you fine-tuned.”
—Ian Fischer, co-founder and CEO of Poetiq, Y Combinator interview, 2026

As a result, the new models lit your fine-tuning investment on fire.

Poetiq took the opposite approach. Instead of modifying the model itself, they built on top of it. They describe the harness as “stilts”: the frontier models aren’t competitors; they’re what you stand on. And the key insight is what happens when the next model ships: that same harness slots right on top of the new model and immediately performs even better. No retraining. No new dataset. The harness compounds.

This is the asymmetry that matters:

• Fine-tuning is a depreciating asset. The moment the next model drops, your investment resets to zero.

• A cognitive harness is a compounding asset. Every model release makes it stronger.

And that asymmetry is the part the labs can never absorb. The labs absorb techniques, because techniques are identical for every user. A harness is not a technique. It is your unique judgment written down: which thinking moves fire, at which step, on which problems. Consensus can be pretrained into a model. Your deviation from it has no shared signal to train on, because it is yours alone.

The surprising part is what building one doesn’t require: a lab, a research team, a compute budget. What it requires is knowing how to organize thinking in a specific domain. Which is exactly what most experts have spent their careers learning to do.

So, I wondered how I could go beyond a mental model system prompt. Then, I came across a fascinating case study.

A Famous Tech CEO Took AI Mental Models Further Than I Imagined

A single sentence upgraded one answer. A system prompt upgraded every answer.

But both still work one answer at a time.

It became clear to me that the next leap was to wire different models to different stages of a whole process, the right specialist at the right step. By pointing this at the workflow that creates articles like this one, I saw an opportunity to drastically increase the quality and quantity of my writing.

But I had never done something like this before, so I wasn’t sure how to think about doing it exactly.

That’s when I found out about a tool called gstack created by Garry Tan.

Tan is the president and CEO of Y Combinator, the largest tech accelerator in the world. It’s behind companies like Airbnb, Stripe, Reddit, OpenAI, DoorDash, Dropbox, and Coinbase. (Of those, OpenAI is the only one that didn’t come up through a YC startup batch; it launched as the nonprofit “YC Research” while Sam Altman was running YC.) He has been building products for 20 years as a tech entrepreneur, and he is shipping more than he ever has. In one recent 60-day stretch, by his own account, he created three production services and 40-plus features, part-time, while running YC. By his own logical-lines-of-code accounting, he is now shipping at roughly 810 times his 2013 daily pace, and his year-to-date output for 2026 alone already exceeds his entire 2013 total by about 240 times, which is still absurd.

One of those tools was gstack, and it has crossed 120,000 GitHub stars, making it the 77th most-starred GitHub project of all time. Open the repository expecting clever engineering and you find almost none. It is nearly all plain text.

What is inside is not a prompt. It is a company. gstack turns one AI into a team of 23 specialists and eight power tools that runs every project through the same seven-step sprint, each step handing its work to the next:

1. Think. Pressure-test the idea and decide what is worth building.

2. Plan. Turn it into an architecture and a scope.

3. Build. Write the code.

4. Review. Hunt for bugs and weak spots.

5. Test. Run it for real and probe it for security holes.

6. Ship. Version, document, and release.

7. Reflect. Log what was learned for next time.

The engine of each step is not code. It is mental models. At every step, the specialist running it fires a specific set of named thinking tools, each wired to the moment it helps. In planning alone, three different reviewers each fire a different shelf. Ask the CEO reviewer to pressure-test a plan and it runs, in order, Jeff Bezos’s test of whether a choice is a reversible “two-way door” or an irreversible “one-way door,” Charlie Munger’s inversion (”what would make this fail?”), Steve Jobs’s focus as subtraction (he cut Apple from 350 products to 10), and Sam Altman’s question of where the real leverage is. Hand the same plan to the engineering reviewer and a different shelf fires: Dan McKinley’s “every company gets about three innovation tokens,” Martin Fowler’s “refactor, don’t rewrite,” Fred Brooks on separating real complexity from the kind you invented. Hand it to the design reviewer and you get Dieter Rams, Don Norman, and Steve Krug’s “don’t make me think.” Counted up, that is dozens of named mental models, borrowed from dozens of different operators, each assigned to the moment where it earns its keep.

This is the difference between using a model and building a system. A system prompt fires your models on one conversation. gstack fires the right models at the right step across an entire pipeline, every time, with no one having to remember to. None of it made the underlying model smarter. It is the same model everyone else types into. What Tan did was write his whole judgment down as named heuristics wired to a sequence, so the right lens fires on every task instead of only on the days he remembers to reach for it. He gave the machine his mind as a system, and it now runs that mind at a width, and a consistency, he never could.

Tan did this for code. I did it for the one thing I have given my life to: thinking and writing with mental models. Earlier I confessed that even with a head start, I could never run the latticework in my own head. This is what I did instead of giving up on it: I built a system to run it for me.

Everything I have learned and coined about how to think and write, I have been writing down as a set of skills an AI runs. Dozens of them now. One carries my writing voice. One runs the analytical model I use to read the news. Others hunt for quotable lines, pressure-test a draft sentence by sentence, and sharpen every subheader. Each is a piece of my judgment in my core specialty, wired to the moment it helps, so my best thinking is present by default instead of only when I remember to reach for it. The article you are reading went through that system. It is not a description of the method. It is the method’s output.

What Tan and I both stumbled into is bigger than code or writing. It is the clearest case yet of a kind of thinking that was never possible before, and the cleanest way to see it comes from the most influential psychologist of the last 50 years...

Beyond Nobel Laureate Daniel Kahneman’s System 1 And System 2 Thinking. Meet System 3.

Daniel Kahneman is famous for popularizing System 1 and System 2 thinking in his New York Times bestselling book, Thinking Fast And Slow.

In it, he distinguishes between two types of thinking:

System 1 relies on our associative memory. It uses connections between earlier seen words, images, actions, and emotions to form a conclusion and make quick decisions. In certain situations, System 1 thinking can even be more accurate than System 2 thinking. In other situations, it makes silly errors.

System 2 thinking is the slow, deliberate, and analytical mode of human cognition. It requires conscious mental effort, focus, and energy, making it responsible for complex problem-solving, rational decision-making, and critical thinking.

Using Charlie Munger’s system with AI falls into a third category: System 3 thinking. System 3 is using AI not to replace your thinking, but to augment it: holding more than any one mind can hold, while structuring the thinking process according to your own expertise. You can think of System 3 thinking as ultra-thinking where you:

• Look at something from many perspectives and paradigms.

• Identify and challenge assumptions.

• Use multiple mental models in sequence.

• Attempt to cancel out every blindspot and bias.

System 3 has the speed of System 1 and the intelligence of System 2. And, as AI gets better, the gap between System 3 and Systems 1 and 2 will increase.

For all of human history, System 3 was a fantasy. No mind was ever deep enough, fast enough, or wide enough to hold it. That is the real reason almost no one could run Munger’s latticework. Not for lack of wanting. For lack of hardware.

The implications of this are profound…

Implication #1: Cognitive Harnesses Have The Potential To Usher In A New Era Of Democratized And Amplified Intelligence

Remember the question that guided my research a decade ago: the horse-drawn plough reduced the importance of physical strength in the world, so what is the plough for the mind?

I can finally give you my answer. The cognitive harness is that plough.

The reason that question mattered to me went beyond my own career. If a tool could augment intelligence, then the advantages of intelligence could finally be spread.

And the plough analogy is more precise than it first appears. The plough didn’t make a single farmer stronger. It made strength less decisive. Before it, the land a family could work was governed by muscle. After it, an ordinary farmer behind a plough out-produced the strongest man in the village working without one. The tool didn’t upgrade people. It downgraded the trait.

A cognitive harness does the same thing to raw intelligence. It doesn’t raise your IQ. It makes raw IQ less decisive, because the heavy cognitive lifting — storing thousands of models, retrieving the right ones at the right moment, firing them in sequence, checking the work — moves out of your biology and into the system. At the same time, it also helps make you smarter.

To feel why that’s a democratizing force, look at who amplified intelligence has belonged to until now.

To run even a partial latticework, you had to win several lotteries at once:

• The genetic lottery. A memory and a processing speed most people are not born with.

• The time lottery. A job where thinking is the work, so you can spend most of the day reading, the way Buffett and Munger famously did.

• The money lottery. The wealth that buys that kind of day in the first place.

• The temperament lottery. Decades of patience for a payoff that compounds slowly and invisibly.

History shows what happens when a scarce ability like that finally gets packaged into a tool:

• Before the printing press, the accumulated knowledge of humanity belonged to whoever could afford hand-copied manuscripts, which is to say, almost no one. Then the price of a book collapsed, and within a few generations, so did the monopoly on knowledge.

• “Computer” was a job title before it was a machine. Complex calculation belonged to trained specialists, and rooms full of human computers powered observatories and space programs. Then the machine version arrived, calculation became effectively free, and today nobody brags about being able to do long division.

Each time, the pattern repeats. The scarce ability doesn’t just spread. It stops being the thing that separates people, and the frontier moves up to whatever the tool still can’t do.

Now recall the two charts near the beginning of this article: a century of rising IQ, then the recent reversal. Every force commonly credited for that century of gains — better nutrition, more schooling, smaller families, more abstract work — operates on the scale of generations.

A cognitive harness is the first intelligence lever in history that operates on the scale of an afternoon. You install it once, and the very next question you ask is processed through more models than any unaided mind has ever run.

Think about what that makes possible for people who were locked out of elite thinking until now:

• The first-generation college student gets the decision checklist that used to require a mentor at a hedge fund.

• The solo founder in a small town gets the strategy pressure-test that used to require a consulting retainer.

• The mid-career manager gets a Munger-grade review fired on every major call, not just the ones she remembers to slow down for.

• The family facing a hard decision gets their reasoning stress-tested from ten angles before they walk into the room where it gets made.

To be clear, democratized does not mean flattened. Amplifiers never abolish advantage; they relocate it. The advantage stops living in what you were born with and starts living in what you build on top of the amplifier. More on that in a moment.

But make no mistake about the direction of the change. The floor is rising faster than at any point in the history of thinking. Anyone willing to learn the system can now run a wider latticework than Munger ever did — because Munger had to run his only on three pounds of tissue, and you don’t.

Implication #2: The Only Ceiling Left Is the One You Set Yourself

Mine was handed to me at eight years old, one point short of a program, and I unconsciously spent the next 30 years trying to outwork it.

By the test’s own logic, I was never “not smart.” One point short of the gifted line puts you near the top. The number was never what held me back. What I decided it meant did.

For most of history, most verdicts like that came with an alibi. You could point to your genes, the “hardware” you were issued, and say the ceiling was real and external. The latticework stayed out of reach for lack of hardware, and that was physics, not failure.

The alibi is gone. The limit that made the latticework impossible has been lifted for almost everyone at once. The ceiling that remains is no longer the one reality assigned you. It is the one you assigned yourself. You can think far past what you were told, and you can no longer blame the number if you don’t.

We are drawing a new line right now, not by genes this time, but by who learns to run these systems and who does not. The person fluent in System 3 thinking will out-decide, out-create, and out-earn the person who takes the first answer the machine gives, and the gap will look like the old one between the gifted and the rest.

With all that said, I want to officially announce and share the second version of the mental model cognitive harness…

Announcing Version 2 Of My Mental Model Operating System

Earlier this year, I released a skill called Mental Model Recipes. It organized more than 100 mental models into 40 thinking “recipes,” sequences of structured moves that gave Claude answers with depth and nuance that the base model couldn’t reach on its own. It became the most popular AI skill I’ve ever shared.

This is version 2 created in collaboration with Bonnie Johnston.

Here’s what’s new…

When you ask a question, it doesn’t just answer it. It runs your question through a six-step process before it ever gives you a response. Each step sharpens its thinking. Let me walk you through what happens under the hood.

Step 1: It diagnoses what you’re actually stuck on.

Not what you asked, but what’s underneath the asking. The skill classifies your situation into one of 10 kinds of stuckness: you can’t choose, you can’t create, you can’t diagnose, you can’t predict, you can’t persuade, you can’t understand, you can’t execute, you can’t align, you can’t grow, or you can’t change the system.

This matters because the kind of thinking tools you need depends entirely on what kind of stuck you are. A decision problem and a creativity problem need completely different mental models.

Step 2: It identifies the worldview you’re operating in.

This is the water you’re swimming in: the paradigm, the assumptions, the mental map of reality you’re using without realizing it. Your worldview shapes what you notice and what you miss, and it’s almost invisible from the inside.

Step 3: It fetches the opposition.

Here’s why this step exists. There’s a catch to having one AI look at your question from many perspectives: all of those perspectives come from the same mind, and that mind wants to agree with you. Left to its defaults, AI bends every lens toward the way you framed the question. You can get what feels like a unanimous panel and is actually one voice wearing 300 costumes. And that false unanimity is most dangerous at the exact moment you need dissent most: right before a big decision.

So we built the disagreement in. From 338 rival pairings, the skill finds a worldview that sits in tension with yours and asks: what does this perspective see that yours doesn’t? This is the step that didn’t exist in v1, and it’s the one that surprises people the most. You’re not getting a second opinion from the same angle. You’re getting a genuinely different lens, one that reveals what your own worldview hides. It breaks the echo chamber you didn’t know you were in.

It also gives you a reading rule for every answer the Stack produces: trust the divergence, audit the unanimity. Where the lenses disagree, that fork is the signal. Where they all agree with you a little too smoothly, that’s the moment to slow down.

Step 4: It de-biases your question, not just the answer.

The previous skill de-biased the answer. This one starts a step earlier. It looks at the cognitive biases and logical fallacies implied in the way you framed the question, and then it asks: if you didn’t have those biases operating, what other questions might you have asked? It answers those questions too, and lets those answers shape the response you get to your actual question.

Step 5: It runs the matching thinking recipe.

Based on everything it now knows (your stuckness type, your worldview, the rival perspective, the de-biased question), it selects one of 61 recipes: a specific, ordered sequence of thinking moves built from the full library of 2,375 mental models. This isn’t “here are some mental models that might be relevant.” It’s a structured sequence, chosen for this specific situation, executed in order.

And yes, do the math on that library. The title of this article promised your AI could run 300 mental models. I undersold it. The full build chooses from 2,375.

Step 6: It checks its own work.

It tracks each operation it performed (each model applied, each paradigm surfaced, each bias corrected) and verifies that each one actually changed the answer and added something that mattered. If a step can’t beat the plain answer, the skill is built to say so. Most AI tools never tell you when they’re not helping. This one does.

What you get back is the four-part answer you saw earlier in this article, plus a fifth part:

1. A “natural” answer. The one you would’ve gotten from the AI without the Mental Model OS, so you have a baseline for evaluating the enhanced answer.

2. A mirror of your own thinking patterns. Your cognitive signature.

3. The full reasoning trace. Showing what was selected and rejected, and why.

4. The recipe-driven answer. Not a cleaner version of the first answer. A different kind of answer.

5. A concrete next move you can act on.

One note on when to reach for it. The OS is built for thinking-type questions: decisions, diagnoses, strategy calls, the moments you’re genuinely stuck. For a quick factual lookup it’s overkill, which is why the install gives you a one-word off switch: start your message with “Answer:” and it stands down. And it isn’t fast. You’re trading a one-line reply for a five-part one, on purpose. When a question is worth real thought, that’s the trade you want. When it isn’t, don’t use it.

From 100 Models To 2,375. And That’s Not Even The Big Upgrade.

If you’ve been using Mental Model Recipes, you already know the skill works. Version 2 is what happens when you take that foundation and ask: what’s still missing?

The numbers tell one story. But the three upgrades you’ll actually feel are the ones that don’t have a v1 equivalent at all:

• It argues against you. V1 gave you better mental models for your question. V2 goes looking for a worldview that disagrees with yours and asks what it sees that you don’t.

• It fixes the question first. V1 corrected for bias in the answer. V2 starts a step earlier, stripping the bias out of the way you asked before it responds.

• It grades its own work. V1 had no self-verification. V2 confirms each step actually improved the answer, and tells you when one didn’t.

The short version: V1 gave you the right mental models for a question. V2 first figures out what you’re stuck on, shows you the worldview you didn’t know you were in, drags in the opposition, strips the bias out of the question itself, then checks its own work.

The Test Most AI Skills Would Fail

There are a lot of AI skills floating around the internet right now. The problem is:

• You have no idea what thought was put into them

• You have no idea how specifically they’re designed to fit the creator’s situation or personal workflow

• You can’t always tell if the results are better than what the AI would naturally do with a simple prompt

• They don’t check their work

• They just run a process

We didn’t want to ship something like that.

So we built a testing rubric. We gave it a batch of questions, some we generated ourselves, some were real questions we actually needed answers to, and scored the results. The rubric tested each operation the skill performs in sequence:

• Did this mental model actually change the answer?

• Did this paradigm surface something the previous step missed?

• Did the bias correction add something that mattered, or was it decoration?

The rubric goes deep. For each step in the process, it evaluates whether that specific operation contributed meaningfully to the final answer. Not “was the answer better,” but “was it better because of this step.” That’s a harder question, and it’s the one that matters. If a step doesn’t earn its place, the skill should know it, and so should you.

We also built this self-checking into the skill itself. It doesn’t just run the rubric during testing; it runs a version of that verification every time it answers a question. That’s an unusual feature for any AI tool: honesty about its own limits.

Two Editions

The OS comes in two editions, built for two different ways of working with AI.

• The Chat edition is the curated build: 561 mental models, the full paradigm and rival-pairing system, and the six-step router. You upload it like any Claude skill, and it works in Claude Chat. This is the version to start with if you primarily work in the chat window.

• The Code edition is the full build: all 2,375 mental models, the complete data library, a Python engine, and the ability to split work across multiple agents for deeper analysis. It runs in Claude Code, or in Claude Co-work if you’re newer to the agentic side. This is the version for people who are already working in the agentic paradigm or want to be.

The simple choice: if you live in the chat window, start with Chat. If you’re building in Claude Code, go with Code. Or install both and never miss an opportunity to upgrade your thinking again.

The download links and step-by-step install instructions for both editions, including the auto-trigger setup so the Stack fires on its own, are in the paid section at the bottom of this post.

Paid members of this newsletter get version 2 of my mental model operating system, the most popular AI skill I’ve ever shared. You can access it at the bottom of this post after the appendix.

APPENDIX

1. The Mental Model Studies

2. The IQ Research

3. Developmental Pairs

4. AI-Enhanced Answer Example

#1. The Mental Model Studies

Study #1: Experts think about what mental models are relevant to a problem before trying to solve it. Amateurs don’t.

The first thing the research shows is that expertise itself is largely model-based.

In 1981, Michelene Chi, Paul Feltovich, and Robert Glaser handed physics problems to both novices and experts, then asked each group to sort them.

• The novices sorted by surface, matching inclined planes with inclined planes, pulleys with pulleys.

• The experts ignored the surface and sorted by the deep principle that solved the problem: conservation of energy in one pile, Newton’s second law in another. They weren’t seeing a harder version of the same picture. They were seeing through models the novices did not yet have.

Study #2: Experts with more diverse mental models outperform experts with few models from one field.

The bigger claim, the one this whole piece rests on, is that carrying many models from many fields beats carrying one. The largest test of this principle ran for two decades. The psychologist Philip Tetlock tracked 284 experts making roughly 28,000 forecasts about politics and economics and scored every prediction they made
against what actually happened.

Most barely beat chance. One group did consistently better.

• The generalists did better. Referred to as “foxes,” they pulled from many disciplines and drew on several models at once.

• The specialists did worse. Labeled “hedgehogs,” they ran everything through one big theory.

The hedgehogs didn’t just fail to outperform. They were worse, and more overconfident, especially on the long-range calls inside their own specialty. One powerful model, applied to everything, was a liability. The cure was range.

Study #3: The most creative experts have the widest range of interests.

Tetlock measured the impact of range on judgment. But physiologist Robert Root-Bernstein measured the impact of range on creativity.

Root-Bernstein spent decades comparing eminent scientists with ordinary ones, and the gap was not raw ability. It was range. Two studies particularly stand out:

1. Against a baseline of typical scientists, Nobel laureates in the sciences turned out to be far more likely to pursue the arts: roughly twice as likely to play an instrument, around five times as likely to work a craft, and more than 20 times as likely to act or dance, with visual art and creative writing falling in between.

2. In a 25-year study that followed 40 young scientists, the ones who went on to win Nobel Prizes or election to the National Academy of Sciences had the widest range of outside pursuits, and could explain how those pursuits fed their science. The least successful had the fewest, and treated outside pursuits as distractions.

Range matters because a model carried in from one field is often the missing key in another.

Einstein said it plainly:

“If I were not a physicist, I would probably be a musician. I often think in music. I live my daydreams in music. I see my life in terms of music.”

He claimed he rarely thought in words at all, only in images and feelings that he translated into equations afterward. The theory that broke open physics was built, in part, with tools borrowed from music.

Study #4: Your range of outside interests predicts success better than your IQ.

Range does not just help at the margin. It can outweigh the very things we are taught to measure. In an 18-year follow-up study, the psychologist Roberta Milgram found that a person’s intellectually stimulating, intensive outside interests predicted their career success better than IQ, grades, standardized test scores, or all of them combined. The hobbies pursued out of genuine curiosity, the ones that look like detours, forecast achievement more reliably than the scores we spend an entire education chasing.

Study #5: Storage of mental models is not enough. Retrieval is key.

The last three studies make range sound like a free lunch. It is not. Having the models is not the same as using them.

In a classic experiment, Mary Gick and Keith Holyoak gave people a problem almost no one solves cold: destroy a tumor with rays without destroying the healthy tissue around it. Fewer than 1 in 10 cracked it. But people who had first read an unrelated story, about a general taking a fortress by sending small forces down many roads at once, and who were nudged to use it, solved the medical problem at rates of 75% to over 90%. One model, carried in from war and mapped onto medicine, was the difference between stuck and solved. The same study delivers the most important caveat in this article. When people got the fortress story but were not told to use it, only about 20% made the leap on their own. Holding the right model in your head is not the same as reaching for it at the right moment.

Said differently, the science does not say that cramming your head with models from every discipline automatically makes you smarter. Mostly it says the opposite: the hoped-for “far transfer,” where training one skill lifts unrelated ones, barely shows up once studies are run with proper controls. The polymaths in those studies were not passive beneficiaries of their range. They actively reached into one field, pulled out a model, and mapped it onto a problem in another, which is the move that pays.

The bottleneck was never storage. It was retrieval.

Study #6: The basis of expertise is the number of stored mental models.

The pattern holds across fields.

Chess masters do not have better memories than the rest of us. Shown a real game position for a few seconds, a master rebuilds most of the board while a novice gets only a handful of pieces. Scatter those same pieces at random and the master’s edge nearly vanishes, because what the master stored was never the pieces. It was the patterns, the models of how pieces relate.

More recently, researcher Daniel Simons replicated the experiment with chess grandmaster Patrick Wolff and posted it on Youtube. Wolff’s attempt in the random position is almost pathetic. In the real position, it is a miracle. Only two pieces are misplaced, and they’re only misplaced by one square.

Also, notice how Wolff describes the feeling when he’s chunking. Rather than describing the process of working with chunks as difficult, mechanical, or deliberate, he describes it as flowing and unconscious. This is a hallmark of mastery.

Study #7: The pattern applies to teams as well

The same logic scales past the individual.

In a widely cited (if contested) modeling result from Lu Hong and Scott Page, a cognitively diverse group, people running different models, can outperform a group of the highest-ability individuals on hard problems, because where one model is blind another can see.

What range does inside one head, diversity does inside a team. A room full of the same brilliant model is still one model, blind in all the same places. A room running different models sees around its own corners. Range beats brilliance, whether it lives in one mind or many.

Sources:

https://docs.google.com/document/d/16MrG2LBoQZHj87eclTQaiXO2Dy7Aa5WDedjBIRfWY_Q/edit?tab=t.0

https://docs.google.com/document/d/1U1OvHDmB3dX8P3LSXR6fJp9kDqYnvn5Tj304lEtqIYU/edit?tab=t.0

#2. IQ Research

• An extra hour of sleep has a surprisingly large impact on IQ. For example, the performance gap caused by an hour’s difference in sleep is bigger than the gap between a normal fourth-grader and a normal sixth-grader. Which is another way of saying that a slightly-sleepy sixth-grader will perform in class like a mere fourth-grader. “A loss of one hour of sleep is equivalent to [the loss of] two years of cognitive maturation and development,” pediatric sleep specialist Avi Sadeh told the authors of NurtureShock. Sadeh was the lead author on a 2003 study that monitored swings in the cognitive performance of 77 school-age children. Neuroscientist Dr. Tara Swart cites research showing that disturbances to healthy sleep patterns can cause your IQ to drop by 5 to 8 points, and that missing out on just a single night of sleep can reduce your IQ by one standard deviation, or 15 IQ points.

• Regular exercise improves neuroplasticity and memory while slowing cognitive decline. Research suggests that physical exercise may trigger processes facilitating neuroplasticity, enhancing your capacity to respond to new demands. One year of aerobic exercise in a large RCT of seniors grew hippocampal volume and improved spatial memory, with other trials showing slowed gray-matter loss and better brain connectivity over 6 to 12 months (sources).

• Creatine helps the brain most when it’s running on empty. In a 2024 trial, a single dose helped sleep-deprived adults hold on to their working memory and processing speed. In well-rested people the effect is small, and regulators haven’t endorsed a general cognitive claim, but under stress, the benefit is real.

#3. Developmental Pairs

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#4. AI-Enhanced Answer Example

Artifact #1: Plain Answer

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Artifact #2: Better Prompt

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Artifact #3: Cognitive Signature

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Artifact #4: Reasoning Trace

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Artifact #5: Recipe-Enhanced Answer

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The Mental Model OS: Downloads And Install Instructions

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Tomorrow, at 12pm EST, my coach and dear friend Anand Rao, is teaching the second session of Augmented Awakening 2.0. The details to join it are at the bottom of this post. The article below is a stand-alone post, and it sets the context for why the class is so important right now.

FULL ARTICLE

Over the last month of the Agentic Academy, I’ve had a front-row seat to helping non-technical people transition from AI chat to agentic AI. So far, we’ve conducted 15 hours of classes and 20+ hours of one-on-one sessions. Therefore, we understand EXACTLY where people normally get stuck.

What I’ve learned has surprised me…

There is a hidden curriculum that’s necessary to successfully adopt agentic AI

At a fundamental, basic level, here’s what I think is happening:

• There is a technical and emotional skill set that is necessary for making the transition to agentic AI.

• But almost no one teaches it.

• So users get stuck.

Here’s a more nuanced take:

The technical skill set to adopt agentic AI is so commonplace among AI lab employees that they suffer from the Curse of Knowledge. It’s hard for them to empathize with nontechnical users. Therefore, they miss things that are obvious to them, but not obvious to their users.

Not only that, I think they are so focused on industry publications and X posts from frontier users that they are unaware of the magnitude of the plight of nontechnical users without lots of followers.

As a result, they make several mistakes:

• They don’t even think to mention key skills and steps in their tutorials.

• They create software that is so complicated that it feels unusable for non-technical people to figure out on their own.

• They furiously add new features while making minimal improvements to their onboarding experience.

The result for the masses is often confusion, information overwhelm, and feeling behind. And the ultimate result is this…

The democratization of agentic AI will stall unless something changes…

Most knowledge workers are non-technical. They aren’t coders, data scientists, or IT administrators. They write emails, Slack messages, have meetings, create presentations, generate proposals, and so on. They use the same software over and over. Understanding the details of how computers work isn’t necessary.

If anything, you could argue that people have gotten less technical over the last 15 years because Apple pioneered extreme usability, which made it possible for computer users to not be technical at all. For example, consider how amazing and unique iOS is in computing history:

We take it for granted now. But think about the individual usability innovations that Apple stacked on top of each other:

• Discovery. When you want new software, you find it in an easy-to-navigate store with reviews.

• Security. They take care of security, so you don’t have to worry about spam anymore. Remember shareware in the 90s?

• Installation. When you want to install software, you simply click one button. You don’t have to navigate the file system on your computer in order to find the software to install it after you download it.

• Privacy. Apple has also built a track record for privacy. So, it’s possible to use Apple devices without giving away all of your personal information.

• Data Backups. Even if you lose your phone, all of the data will be there, and it will be easy to re-add.

• Maintenance. Updates install overnight. No drivers, no version conflicts, no settings files. You take care of your work; the phone takes care of the phone.

Agentic AI takes multiple steps backward on each of these fronts:

• Discovery. There is no app store. Skills, agents, and MCP servers are scattered across GitHub repos, Discord servers, and X threads. No ratings. No reviews. No way for a non-technical person to tell a gem from a trap.

• Installation. I’ve now helped 100+ people install Claude Code. Most nontechnical people cannot do it on their own. Others often need up to an hour to make it work.

• Security. Nothing you install has been vetted for you. Download an external skill, and you risk prompt injection—hidden instructions that can turn your own AI against you. It's 90s shareware all over again, except this time the software can read your files and act on your behalf.

• Privacy. Both Claude and OpenAI train their new models on your data by default. You can opt out of this, but that’s buried several clicks deep, and most people don’t know it exists.

• Data Backups. Agentic AI invents a brand-new way to lose your data: the agent itself. One misunderstood instruction, and an AI can delete, distort, or overwrite files. Last summer, an AI agent famously wiped a company's production database during a code freeze. Then it told the founder everything was fine.

• Maintenance. The updates come weekly, if not daily. Many of these updates include new bugs and confusing interface changes. You are now your own IT department.

As a result, when non-technical people explore agentic AI on their own, they almost universally get stuck:

• They hear about the agentic AI hype.

• They don’t really understand the downsides of chat or the concrete benefits of agentic AI.

• So they ignore the hype for awhile.

• Eventually, they try it out.

• Then, they quickly hit roadblocks.

• When they search for help, it still doesn’t work.

• After enough painful roadblocks, they simply give up.

• They associate doing technical things with emotional overwhelm and pain.

• They conclude, “I’m not technical. And this isn’t for me.”

• They fall more behind.

The sad thing is that many people blame themselves rather than the software.

Because of this painful loop, I now think differently about the future of AI. Here’s what I believe:

• Agentic AI will continue to become more and more powerful at an ever-faster rate. This means that individuals on the frontier will be able to 20x their productivity or build one-person, one-million-dollar companies in the near future.

• Agentic AI will hit an adoption ceiling way, way sooner than chat AI. People who get external coaching/support, who are particularly motivated, or who are really good at learning how to learn will adopt it. Most won’t.

• We’re not on the path toward a universal AI utopia that most lab leaders say we are. The technology is not on a path toward democratization. There will be an increasing divide between people who adopt agentic AI and people who stay in chat. Said differently, there will be a small class of people who command more income than ever. There will be a large class of people who find themselves more and more disempowered relative to others.

But there’s good news

First, the hard parts of using agentic AI are all frontloaded. Once you get through the first 10 hours or so, you can truly hit the ground running forever.

Second, it’s not as hard as it might seem. Each step individually isn’t rocket science. With the right support, you can get through it.

Finally, the info you need is out there. You don’t have to rediscover it or do painful trial and error. There’s a necessary cognitive skillset and an emotional one. You need both. We cover the cognitive skillset in the Agentic Academy, but in this email, I wanted to talk about the emotional skillset, because it’s essential and no one else talks about it.

The hidden emotional skillset that’s sabotaging your AI success

After enough setbacks trying to do technical things, most people associate learning technical stuff with anxiety, overwhelm, and burnout. The result is avoidance, paralysis, and procrastination.

Just going near it is triggering. Doing it is painful.

No matter how smart you are, it’s hard to do or learn anything consistently when you’re completely overwhelmed.

Two related mental models help us understand the challenge:

#1. Mihaly Csikszentmihalyi’s flow model

#2. The Comfort Zone / Growth Zone model

As they relate to AI, both of these models point to the same underlying issue. Growth stops when we get too overwhelmed. For 95% of knowledge workers, agentic AI is in the anxiety / burnout zone. As a result, they get paralyzed with agentic AI.

To succeed in the agentic era, I now believe that individuals need to learn how to get out of the red zone.

Below are the three primary steps to do so:

1. Reduce the knowledge gap. Get an understanding of the situation and the next step.

2. Reduce overwhelm. A feeling has already shut the door, and no amount of correct information gets through until it shifts.

3. Build a habit of using AI whenever you’re stuck to get unstuck. AI is an incredible support system for getting unstuck. But it’s easy to forget we can use it as a resource. Once this habit of using it exists, getting stuck stops meaning 'I'm not technical' and starts meaning 'time to ask AI.'

To help with this skill set, master coach Anand Rao, will be leading a session tomorrow on the topic for paid subscribers of this newsletter.

PAID SUBSCRIBERS:
Join A 90-Minute Session Tomorrow (Friday) At 12:00pm EST To Learn The Emotional Skillset Of AI Adoption

The class is Session 2 of Augmented Awakening 2.0, the year-long monthly program I co-teach with Anand Rao. It’s included with your paid subscription to this newsletter.

Anand is my coach, mentor, and dear friend, and he has spent 30+ years studying transformation and change. His specialty is exactly what I described above: the feelings that shut people down and the tools that get them moving again.

Last month, Anand taught prioritization: choosing what to aim your agents at when you can build anything. (We watched his 20-project list collapse into 2 decisions.) This session is about what happens after you aim: the moment you get stuck. You don’t need Session 1 to get value from this one.

Here’s what you’ll have by the end of class

• A way to quickly tell which kind of stuck you’re in.

• A next step, when it’s a knowledge gap. Describe where you are, get one concrete move back. Not a tutorial. One step.

• A way through when it’s a feeling. A few taps, a touch point, or a breathing pattern, matched to how the feeling shows up. This is the piece hardly anyone else teaches.

• An AI tool you can install and keep, just like last month. It walks the loop with you and keeps a quiet record of your patterns.

• The start of the habit: reaching for help instead of away from it.

How to prepare for the session

Bring something real that you’re stuck on.

The session time and date

• Date: Friday, July 17, 2026

• Time: 12:00pm-1:30pm EST

How to join the session

The live and on-demand classes are available to paying members of this newsletter. All you have to do is become a paid subscriber:

Subscribe now

Once you become a paid member, you can use the following Zoom info to join:

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What if I told you that you and I could get better results from AI than the software engineers working in the labs that made it?

They have the data, the talent, the compute. Shouldn’t they win hands-down?

I believe that there’s a massive, underexploited opportunity to make AI smarter than the labs inside your own niche.

And you don’t do it by fine-tuning AI, which is expensive and time-consuming (and out of reach for most of us). Instead, you beat the labs by building on top of what they’ve already built.

The evidence for this opportunity is mounting.

For example, a six-person team called Poetiq took Gemini 3 Pro, wrapped it in what they call a “cognitive harness,” and beat Gemini’s own flagship reasoning model on ARC-AGI 2 at half the cost.

Poetiq isn’t alone. In today’s session, I share several case studies and evidence from 10+ studies.

Today, they demonstrate that mental models, paradigms, heuristics, and other t…

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To summarize the offer, it’s a 10-week cohort that migrates you from using AI as a chatbot to running an agentic system built around your own work, with white-glove support every step of the way. Starting in Claude Code and structured so it transfers to Codex.

Created by two of the leading writers and teachers in AI knowledge work, it is the first and only cohort of its kind. Because it is not a course you watch. It’s a concierge-style program where we guide you, hands-on, through building a custom AI work process/workflow tailored to your specific work, domain, goals, actual files, examples, and standards. Instead of manually carrying context, instructions, examples, and corrections into each AI session, you’ll have a process that already knows what to look at and how the work should be done.

Bottom line: This program is designed to save you time from recurring work that eats your week, so you can focus on your zones of genius and passion.

Enroll Now

Learn More

Let me get straight to the thing that matters most

Most programs are buyer-beware. You shoulder multiple risks:

• Will you actually be able to find the time to take the course?

• If you do, will it fulfill the promises that its sales page lists?

• If you’re nontechnical, will you be able to succeed with something that’s technical?

You pay, and if you don’t get the result, that’s on you.

We’re doing the opposite:

• One-on-one support. If you’re committed — you show up to the classes and the group support calls — and you still feel stuck, you get unlimited one-on-one calls with us until you get the result you came for.

• 30-day guarantee. And if at any point in the first 30 days you decide it’s not right for you, for any reason, you get every dollar back.

• Result guarantee. After the course is done, if you successfully create a workflow, and you don’t see how the workflow and the skillset were worth the time and money you invested, you can get 100% of your money back.

We built it this way because we’ve been on the other side. I’ve personally spent tens of thousands of dollars on programs that never panned out:

• When I was in college, I signed up for a $2,000 real estate investing program at a Mark Victor Hansen conference. I didn’t even have the money or want to be a real estate investor. However, the offer was so compelling that I got swept up in the moment and bought it on credit. I never even opened the packaging for that course.

• More recently, I signed up for a $20,000 program. The material was great, but it just wasn’t the right fit for me at that time.

• I’ve probably bought 200+ books that I never picked up.

In the end, investing in transformation requires multiple leaps of faith:

• A bet on yourself

• A bet on us

• A bet on the topic

With this program, we’re flipping the equation so you don’t need to take such a large leap of faith. Here’s why…

Why We’re Putting Our Skin In The Game

Over the last 9 years, I’ve launched 4 cohort programs:

• Learning Ritual Course

• Mental Model Club

• Month To Master

• Blockbuster Thought Leadership Course

The Agentic Academy is the most important one for three reasons:

1. Agentic AI is the most important work technology in history. More than the computer. More than the Internet. It’s critical for every knowledge worker. Not because it gives better answers, but because it can change how work itself gets organized.
   

2. The timing is right. When you adopt tech matters just as much as how you adopt. Adopting the right technology early means you command a premium, have less competition, and get access to the best opportunities before things get crowded.
   

3. There is nothing like it. To my knowledge, there is literally no other program in the world where someone who is nontechnical can get this much personalized support to make the shift to agentic AI:

   • 24/7 community

   • Weekly class

   • Weekly group tech support call

   • Ongoing mastermind

   • 7+ AI skills (that do the work of installing agentic AI for you).

   • One-on-one support (more on this below)

Because we believe in the program, we’re putting skin in the game that I’ve never seen a course at this price point offer:

#1. Unlimited one-on-one support

Since this is our first cohort and we believe in the program, we’re pulling out all the stops to make sure everyone who is committed gets the results. If you attend the classes and group support calls and still feel stuck, you can get access to unlimited one-on-one calls with Bonnie until you get the result you want.

We don’t offer this benefit lightly. It’s possible that we may lose money. Time is our most precious resource. But, we think guaranteeing that committed students get the result is worth it. And, we’re willing to take the leap of faith.

#2. 30-day FULL money-back guarantee

If you sign up for the program, and at any point in the next 30 days, for any reason, you feel that the program isn’t right for you, you can get a full refund.

#3. Results guarantee

Besides money, the biggest hesitancy I hear about switching to the agentic paradigm is the time it takes. They’re busy.

Ironically, that is exactly what the agentic paradigm does. It saves you time.

That’s why we’re making a bold guarantee. After the course is done, if you successfully create a workflow, and you don’t see how the workflow and the skillset were worth the time and money you invested, you can get 100% of your money back.

Last Chance

If you’re on the fence, these guarantees exist for exactly this moment. Step in. If it’s not right, you’ll know within 30 days, and you’ll get everything back.

The cohort starts tomorrow. Enrollment closes tonight.

Enroll Now

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But if you haven’t used Anthropic’s Claude Code or OpenAI’s Codex, then agents still feel abstract. And, it’s hard to commit time to something where the benefits aren’t concrete.

Then, I saw the incredible demo, which I share in today’s post.

For context:

• Earlier this year, we hosted of on Cozora.

• Wyndo runs the fastest-growing AI newsletter on Substack, and he has been using Claude Code since it came out.

• Cozora is an organization I co-founded with and . Every week, we share a top AI skill of a top AI knowledge worker, entrepreneur, and creator, so you can supercharge your AI productivity.

Wyndo walked us through his setup and how he works with Claude, step by step. Everyone in the Zoom said it was one of their favorite Cozora sessions.

I was so impressed by the presentation that, soon after, I pitched Wyndo on creating a program together to help non-technical knowledge workers make the shift to agentic.

He loved the idea.

It’s now a few months later, and we’re launching the Agentic Academy For Knowledge Work this Monday, June 22. It feels surreal that the idea is now a reality.

So, I asked Wyndo if I could share his demo video and an explainer article about it with my subscribers. He agreed.

Over to Wyndo…

The Claude Code Setup Behind My Newsletter

By Wyndo

Most people are still trying to get better AI output from better chats.

I get why. That is where most of us started.

You open ChatGPT, Claude, Gemini, or whatever tool you like. You ask a better question, add more background, paste in a few examples, and try to explain what good output looks like.

Sometimes it works really well.

But then the next chat starts from zero again.

You re-explain the project, re-upload the files, paste the same rules, correct the same mistakes, move the output into Google Docs, Notion, email, Substack, Slack, or wherever the real work happens.

At some point, the problem is no longer the prompt.

The problem is that the AI has no real connection with your work.

That was the main idea I tried to show in a past session with the Cozora Community. I walked through how I run AI Maker from inside Claude Code, including my newsletter folder, my project structure, my CLAUDE.md, my skills, my slash commands, my memory rules, and the way I use agents to help with research, writing, planning, and operations.

This was a live tour of the system I actually use, and now I’m sharing it with you.

When people hear “Claude Code,” they often assume this is for programmers. Margaret, one of the people in the session, asked the obvious question right away:

“So is Claude Code like about coding?”

That is the question most normal people have.

And the answer is: it started there, but that is not where it ends.

Claude Code is useful because it lets an AI agent work inside a real project folder. It can read files, follow rules, create outputs, run repeatable workflows, store a large amount of information, use tools, remember corrections through files and instructions, and help you move from “answer this question” to “help me run this part of my work.”

That is a different relationship with AI.

The Shift I Was Trying To Show

For the last few years, most AI work has looked like chat.

You are the middleman.

You ask a question, get an answer, copy the answer, paste it somewhere else, open another app, bring back more context, ask again. Then you rinse and repeat.

That is still useful. I still use normal chat sometimes, especially on mobile.

But for my day-to-day work, the center of gravity has moved.

My newsletter now lives in a folder Claude Code can read, including the drafts, archive, writing rules, audience notes, paid-versus-free rules, and performance data.

So when I ask for new post ideas, Claude does not have to guess from the prompt alone. It can inspect the actual project, read the files that matter, see what I have already published, and follow the rules I wrote for the newsletter.

That’s the transformation you can expect when you go full AI‑agent mode.

💡 Quick note: This is also the shift Michael Simmons and I are teaching in Agentic Academy for Knowledge Work.

AI chat has a ceiling. You re-explain the project, re-upload the files, repeat the same corrections. Agentic Academy is 10 live weeks where you build one AI system that runs your real work. No coding. Starts June 22. Enroll now.

Learn more about Agentic Academy

Watch The Video

This replay is a walkthrough of the system behind my newsletter and project management setup.

I would not watch it like a normal webinar where you try to remember every tool name. I would watch it with one question in mind:

What would my first agent folder need to understand about my work?

That is the useful lens.

Because the specific tools will change. But the underlying pattern is going to matter for a while:

1. Give the agent a folder.

2. Put the right source material inside.

3. Write the rules it should follow.

4. Create repeatable workflows.

5. Add guardrails.

6. Improve the setup every time the agent gets something wrong.

That is the pattern I kept coming back to during the session.

What We Covered

The session started with a question from Julia about managing multiple systems together.

She was already deep in the work, cleaning up a bunch of Claude projects, trying to build a better master project, and thinking through how memory and project status could work across different kinds of work.

That was useful because it surfaced one of the real problems with AI agents:

They can do a lot, but they can also do too much.

Julia mentioned that Claude Code can sometimes “go nuts” and do far more than she asked for. I have felt that too. Sometimes the agent understands the intent and runs with it in a useful way. Sometimes it does a long chain of work and gives you something that is technically impressive but not what you wanted.

That is why the folder matters.

You need a place where the agent can learn:

1. What this project is

2. What counts as good work

3. Which files matter for which tasks

4. What actions are allowed

5. What mistakes it should not repeat

6. When it should ask before moving

That is the job of the system around the agent.

In my setup, the most important file is CLAUDE.md.

I described it in the session as the brain of the project. It is the file Claude reads first. It tells Claude what the project is, what folders exist, what rules matter, what reference files to use, and what standards to follow.

For AI Maker, that means Claude knows things like:

1. Newsletter drafts live in one place.

2. LinkedIn posts have a different style than newsletter posts.

3. Paid posts need implementation detail.

4. Free posts should be complete on their own.

5. My writing should avoid polished corporate language.

6. If a task involves audience data, it should read the audience files first.

The more I use it, the more I think most people should start with the file that teaches the agent how to understand the project. The complicated automation can come later.

The Six Pieces I Walked Through

During the replay, I broke Claude Code into six pieces.

You do not need all of them on day one. I definitely did not build all of this at once.

But once you see the pieces, the whole thing feels less mysterious.

1. CLAUDE.md

This is the main instruction file for the project.

It tells the agent:

1. What the project is.

2. What rules matter.

3. What files to read for different tasks.

4. How the folder is organized.

5. What quality standards to follow.

For my newsletter, this is what stops Claude from treating every request the same way. A newsletter draft should load different context than a LinkedIn post. A strategy question should load different files than a quick copy edit.

Without this file, Claude has to infer too much from the chat.

With it, the agent starts from the project itself.

2. Skills

Skills are reusable workflows.

The example I showed was my AI News Intel skill. It connects to my Gmail, fetches AI newsletters from a specific label, reads the recent emails, summarizes the themes, and turns them into content opportunities for AI Maker.

The news summary is useful, but the repeatability matters more. I no longer have to write the whole prompt every time.

I can trigger the skill, give it a time range, and let it run the workflow. If the output is not useful, I can update the skill so the next run gets better.

That is different from saving a prompt in a note somewhere.

A prompt is something you remember to reuse.

A skill is a small workflow the agent can run.

3. Slash Commands

Commands are simpler shortcuts.

I use them for repeated actions where I do not want to explain the same thing again. A command might analyze a draft, generate notes, start a review, or run a recurring process.

But some people still find it hard to tell the difference when to use commands vs skills.

My current version is simple:

1. If it is a small shortcut, it can be a command.

2. If it is a full workflow with multiple steps, references, and quality checks, it should probably be a skill.

This rule works really well for me.

4. Memory

Memory is where corrections start to compound.

When Claude gives me an output that feels wrong, I do not only fix that one output. I ask it why it made the choice, what preference it missed, and how that preference should be saved so it does not repeat the mistake.

That is how the system slowly becomes more like me. I keep turning repeated corrections into written rules instead of hoping the model magically knows my taste.

This is one of the biggest mindset shifts. If you correct the same thing three times, that correction probably belongs somewhere outside the chat.

5. Subagents

Subagents are separate workers Claude can run for different parts of a task.

In the session, Julia asked a good question about this. Why use five agents instead of one? How much direction do you have to give each one?

My answer was that I usually do not want to manually manage five separate agents every time. Instead, I prefer to build that pattern into a larger skill.

For example, a research skill might send one agent to inspect Reddit, another to look at Twitter or X, another to check newsletters, another to summarize, and another to critique the argument. Then the main agent pulls the pieces together.

That is not necessary for every task.

But for work where different sources or viewpoints matter, subagents help keep the work cleaner. Each one can focus on a specific job instead of one overloaded chat trying to hold everything at once.

6. Tools

Tools are the connections that let the agent do real work.

In my setup, that can include Google Workspace, Gmail, Google Docs, Obsidian, GitHub, Tavily, and other command-line or MCP connections.

This is where things start to feel different from chat.

Instead of asking AI to write something and then manually moving it into the real tool, the agent can often create the file, read the folder, generate the document, or pull the information directly.

I still review the work.

But when the tools are connected carefully, the agent can handle more of the boring middle steps.

The Part Beginners Should Pay Attention To

If you are newer to this, the technical words can make the whole thing feel bigger than it is.

Claude Code. Cursor. Terminal. Markdown. GitHub. MCP. CLI. API. Skills. Subagents. Plugins.

That list can make the setup feel like a software engineering project.

But the beginner version is much smaller.

Create one folder for one area of work.

Put the real material inside:

1. Your drafts.

2. Your examples.

3. Your notes.

4. Your rules.

5. Your decisions.

6. Your recurring tasks.

Then create the first version of CLAUDE.md.

That file should answer:

1. What is this project?

2. What does good work look like?

3. What should the agent avoid?

4. Which files should it read for common tasks?

5. What should it ask before doing?

That is enough to start.

In the session, someone asked how to begin if you do not know the right folder structure yet. My answer was pretty simple: dump the relevant files into one folder, run /init, and let Claude suggest the first structure.

Then you brainstorm with it.

You do not need to design the perfect structure before you begin. You need enough structure for the agent to start learning how your work works.

The Privacy Question Matters

Joshua raised the privacy and access question during the session, and I am glad he did.

Because this is the part that can get hand-waved when people get excited about agents.

If an agent can read and act on local files, you need to think about what it should not read and what it should not do.

My current rule is boring, but useful:

Do not put truly sensitive information in the folder unless you are comfortable with the risk.

For API keys, I use separate files like .env.local and make sure the agent should not access them. For tool permissions, I keep some actions restricted. For example, I might let an agent read emails or draft something while blocking send access.

I also try to open Claude Code inside the specific project folder I am working on, not from the top level of my computer.

If I am working on AI Maker, the agent should be inside AI Maker folder. If I am working on another project, that should be a different folder. My journaling and workout files do not live in this newsletter folder because they are different kinds of work.

The goal is to give the agent enough access to help with one real area of work, while keeping the boundaries clear. It does not need your entire life to be useful.

Why I Still Recommend Starting With Claude Code

We also talked about OpenClaw, Perplexity Computer, Claude Dispatch, Telegram, Discord, scheduled agents, and cloud-based agent tools.

Those are all interesting.

Some of them may become better starting points over time. OpenClaw is especially interesting for scheduled work because it can run while you are not sitting in front of the computer. Perplexity Computer is interesting because it can connect multiple models and run in the cloud.

But for most people, I still think Claude Code is one of the best starting places because it keeps you close to the work.

You can see what the agent is reading, approve or reject changes, inspect the folder, and watch it make mistakes—and turn those mistakes into better rules.

You are still in the control.

But this becomes a whole different story if you jump straight to a fully automated agent that runs at 2 a.m., because you need stronger guardrails to ensure the agent doesn’t do things you don’t want it to do, such as sharing private data or sending or deleting emails.

Claude Code is a good training ground for that.

Once you understand the structure, the pattern transfers into other tools, including Codex.

What I Would Do After Watching

If you want to use this session as a starting point, I would not try to copy my full newsletter setup.

That would be too much.

I would start with one area of work where you already repeat yourself.

Good candidates:

1. Weekly planning.

2. Newsletter drafting.

3. Client prep.

4. Research synthesis.

5. Meeting follow-ups.

6. Content repurposing.

7. Project status updates.

Then build the smallest agent folder for that work.

The first version only needs a few files:

1. CLAUDE.md: how the agent should understand the project.

2. examples.md: examples of good output.

3. rules.md: what to do and avoid.

4. source-material/: the files the agent should use.

5. outputs/: where finished work goes.

That is it.

Do not start with five subagents, ten skills, and every tool connection you can find.

Start with the folder.

Then add the next layer only when the friction shows up.

• If you keep repeating the same prompt, turn it into a command or skill.

• If you keep correcting the same mistake, turn it into a rule or memory.

• If one task keeps requiring different kinds of research, consider subagents.

This is how the system grows without becoming too heavy.

Key moments from the session

1. Opening questions: participants share what they are trying to figure out with Claude, Claude Code, and project systems.

2. Margaret asks whether Claude Code is about coding, which becomes the beginner doorway into the whole session.

3. Michael frames the move from chat to agents and shares his own Make.com example.

4. I explain how I run The AI Maker from a local folder Claude Code can read.

5. We talk through Obsidian versus Notion and why local Markdown files are easier for agents to inspect.

6. I walk through the six core pieces: CLAUDE.md, skills, commands, memory, subagents, and tools.

7. I show the AI Maker folder in Cursor and explain why I prefer the visual interface over the terminal for most people starting out.

8. Julia asks how to control scope when Claude Code over-executes.

9. Joshua asks about privacy, file access, and permissions.

10. I demo the AI News Intel skill and show how it turns labeled Gmail newsletters into an AI Maker content report.

11. We discuss plugins as a larger layer above individual skills.

12. Julia asks whether OpenClaw makes this obsolete.

13. We compare Claude Code, OpenClaw, Perplexity Computer, scheduled agents, and multi-model orchestration.

14. The closing recommendation is to start by creating a CLAUDE.md and using /init to help structure your first folder.

Resources Mentioned

1. Claude Code: the main agent tool demonstrated in the session.

2. Claude: used as the broader Claude chat and model family people already know.

3. Claude Code Desktop: the recommended starting path for Agentic Academy.

4. Cursor: the visual interface I recommended for people who want to see files and approvals more clearly.

5. VS Code: another editor that can run Claude Code through an extension.

6. Terminal: the more technical interface for running Claude Code.

7. CLAUDE.md: the project instruction file that tells Claude what the folder is, what rules matter, and where to find context.

8. Markdown: the file format I use because agents can read and edit it cleanly.

9. Obsidian: my local Markdown layer for managing notes and project files.

10. Notion: discussed as a cloud tool I moved away from for this kind of agent setup.

11. Google Docs: discussed in Julia’s example of extracting comments into structured Markdown.

12. Google Workspace: one of the tool connections I use with Claude Code.

13. Gmail: used by my AI News Intel skill to read labeled newsletters.

14. GitHub: described as a backup and collaboration layer for project folders.

15. Git: the version-control layer behind GitHub.

16. Make.com: Michael’s example of a task that took him 20 hours there and 20 minutes in Claude Code.

17. Tavily CLI: mentioned as one way I can run research from inside Claude Code.

18. NotebookLM: mentioned as a tool people might otherwise open separately.

19. Nano Banana: mentioned as an image-generation tool people might otherwise open separately.

20. Asana: mentioned as an example of a project tool that could connect through agent tooling.

21. MCP: one way tools can connect to agents.

22. CLI: command-line interface, another way agents can access tools.

23. API: another way tools and data can be accessed.

24. Skills: reusable workflow instructions for agents.

25. Slash commands: shorter triggers for repeated actions.

26. Memory: saved preferences and corrections that help future sessions improve.

27. Subagents: separate workers used for parallel or role-specific tasks.

28. Plugins: larger bundles that can include skills, agents, tools, commands, and MCP connections.

29. AI News Intel: the skill I demoed for summarizing AI newsletters from Gmail.

30. OpenClaw: discussed as a more automated agent environment with scheduled tasks and memory patterns.

31. Perplexity Computer: discussed as a cloud-based agent tool with built-in app connections and multi-model orchestration.

32. Claude Dispatch: mentioned as part of the move toward triggering Claude Code from outside the computer.

33. Telegram: discussed as a place to trigger or interact with agents.

34. Discord: discussed as another place Claude can connect.

35. Manus: mentioned as part of the broader agent-tool category.

36. Google Ads and Meta Ads: mentioned as examples of tools a marketing plugin might connect to.

37. .env.local: mentioned as a safer place to store API keys outside normal agent access.

38. settings.local.json: mentioned as a way to restrict what Claude Code can do.

39. Substack: the publishing home for The AI Maker and several participants’ work.

40. Cozora: the community where this session happened.

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Most people are using AI to get things done faster. But there’s a deeper question that this session tackles head-on: what does it actually mean to think with AI, rather than just delegate to it?

Augmented Awakening 2.0 is a year-long monthly program co-taught with Anand Rao (developmentalmastery.com) for people who want to develop a genuinely different relationship with AI — one that sharpens your thinking instead of replacing it. We’re past the prompting era. We’re now in the agentic era, where AI can build, execute, and iterate on your behalf. Which means the bottleneck isn’t the AI anymore. It’s your ability to decide what to build, in what order, and why.

This session focused on one of the most practical and underrated skills in the agentic world: prioritization. Not a productivity hack, but a real cognitive skill — the ability to take a genuinely chaotic mental plate and turn it into clear decisions. We built a working AI-powered prioritizer skill inside Claude Code and Cowork, ran it live, and then went a layer deeper: building a meta-skill that improves the prioritizer itself over time. Second-loop learning, in practice.

If you’re someone who could build almost anything right now — and feels the paralysis that comes with that — this session is worth watching in full.

During the class, we:

• Launched Augmented Awakening 2.0, the year-long agentic AI program

• Explored the shift from prompting to leading AI agents

• Introduced the “agent leader” framing — what kind are you?

• Demonstrated a live “brain blurt” as the first prioritization step

• Ran the Prioritizer skill live with Anand’s real messy project list

• Watched 20 projects collapse into 2 decisions and 1 clock

• Installed the Prioritizer skill in Claude Code and Cowork together

• Introduced a meta-skill that reads your logs and improves the prioritizer

• Discussed second-loop learning: skills that evolve how you use them

• Answered participant questions on setup, sequencing, and memory logs

Implications

The skill Anand and I demonstrated in this session sounds deceptively simple: dump your chaos, let AI sort it. But what actually happened in the session was more interesting. When participants ran the Prioritizer, the tool wasn’t telling them what to do — it was asking questions that helped them hear what they already knew. That’s a meaningful distinction.

For years, the promise of AI productivity has been “do more.” What this session points toward is different: think better. Anand frames this through adult development research — the ability to make decisions under conditions of ambiguity, without perfect information, without perfect options, is a maturity skill. The Prioritizer isn’t a shortcut around that skill. It’s a way of building it.

The broader implication is one I keep coming back to: as AI agents get more capable, the leverage point shifts almost entirely to judgment. What do you build? What do you not build? What deserves attention this week versus what can wait until July? The people who develop that muscle — who can turn a chaotic mental plate into a clear sequence — will have a meaningful edge. Not because they’re smarter, but because they’ve practiced the skill with the right tools at their side.

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Mental Models + AI: Unlocking System 3 Thinking

Most people think of AI as a faster way to do things they already do. What if it’s actually a lever for a fundamentally new category of thinking — one that wasn’t possible before?

That’s what this session is about. Over the past decade, I’ve gone deep on mental models — studying how the world’s top thinkers, from Charlie Munger to Magnus Carlsen, build and use conceptual frameworks that others don’t. The pattern is consistent: the people who see what others miss aren’t just smarter. They’ve built a latticework of mental models that lets them spot opportunities, avoid blind spots, and navigate complexity at a speed and depth that seems almost unfair.

AI changes that equation dramatically. When you combine mental models with AI — using them as a command language to direct its reasoning — you get something I’m calling System 3 thinking. Daniel Kahneman gave us System 1 (fast, intuitive) and System 2 (slow, deliberate). Sys…

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Last Friday, I shared that of and I are launching a big, new program for knowledge workers and entrepreneurs who want to make the leap from interacting with AI in chat to building agents that do work for them.

After five consecutive days waking up at 4am EST before my 7-month-old baby, I’m very excited to share that it’s now live!!

The full cohort starts Monday, June 22. But we are also holding an optional prep session on Monday, June 15 at 11am ET.

Check it out

Why We’re Launching This Program Now (In A Nutshell)

The Opportunity

While chat AI increases productivity 1.5x, it has a low ceiling. Agentic AI is already making some professions 20x more productive.

In a few years, all knowledge workers will be using agentic AI. It will be absolutely critical for getting work done, just like electricity and the Internet are now. Many people who don’t learn it by then will be locked out of their profession.

Why Now?

The real choice is not if you should adopt agentic AI, it’s when.

And what you’re actually choosing is whether you want to adopt it before others or after others. In both scenarios, you’ll spend the same amount of time learning it.

But, here’s the thing that most people don’t think about:

• If you adopt it now, you will spend the next few years leading from the front, multiplying your income, getting promotions, and being first to new historic opportunities.

• If you adopt it later, you will spend the next few years feeling behind, stressed, playing catch-up, and trying to minimize the cost of being late.

Every incremental week and month of being early matters when a paradigm shift this big is underway.

The Challenge

Adopting agentic AI is 10x harder than using chat. There are a million roadblocks that non-technical people face in adoption that cause them to say, “Not for me.”

Our Solution

To my knowledge, our program is the first course of its kind that:

• Serves non-technical knowledge workers.

• Is designed from the ground up to be transformative, not just informative.

Classes are just the beginning. This program also includes:

• 24/7 community

• Weekly class

• Weekly tech support call

• Suite of concierge skills that do major parts of the migration for you

• And much more.

We’ve spent 200+ hours creating and testing these skills so that migration to agentic AI goes from long and painful to short and easy.

Why Us

I bring the business and education side. I’ve spent my whole career as an education entrepreneur and have taught 1,000+ classes over the years.

Wyndo brings the systems thinking and tools side. His newsletter is the fastest-growing AI newsletter on Substack. How he teaches AI clearly resonates.

We each have spent thousands of hours learning AI since late 2022, and we want to give you the shortcuts so you don’t have to. Together, we’ve walked dozens of non-technical knowledge workers through this exact transition, and we built this program around what actually worked for them.

In retrospect, the only reason I was early on AI was thanks to three friends:

• In 2020, my close friend Emerson Spartz showed me GPT-3 and told me about the AI scaling laws. I was skeptical, but he insisted it would change the world. He started an AI safety non-profit that year.

• In December 2022, another close friend and mentor, Eben Pagan, gave me a demo of ChatGPT and pushed me to go all in. I was still skeptical, but he gave me another demo and pushed me to explore it more.

• In early 2026, my 20-year friend Tijl Koenderink sat me down and walked me through his best Claude Code hacks and mental models. He knew I would love it once I overcame my initial hesitation about the terminal interface. He was right.

I hope Wyndo and I can be your mentors through this paradigm shift, like Tijl, Eben, and Emerson were for me.

The Goal Of The Program Is Simple

By the end, you will have one real AI process built around your actual work. I can promise you, it won’t be just a demo or another pile of prompts you never use. By the end of the program, you’ll have a working setup where Claude can read the right files, follow your standards, use your examples, remember your corrections, and help you run a repeatable workflow you actually want to keep using.

We start in Claude Code because it is one of the clearest places to learn the pattern: project folders, source files, rules, skills, workflows, permissions, and review.

We also teach the transfer pattern into Codex, so the structure you build in Claude Code can move with you: the project files, rules, examples, skills, workflows, and review habits.

The point of this cohort isn’t to teach you how to master a single tool—it’s bigger than that. The deeper goal is to learn how agentic work actually works, so you can understand what to build, what to ignore, what to trust, and how to keep improving the system as the tools change.

Together, we are trying to make the shift from chat to agents feel less like something you have to figure out alone from scattered tutorials, and more like a guided build on your real work.

Learn More

We created a really comprehensive website to walk your through the offer:

• Curriculum

• The White Glove Migration Suite

• Community & Support

• Testimonials

• Pricing & Enrollment

Founding Members Of This Newsletter Get A $500 Discount On The Agentic Academy

For years, people have asked me how to get everything I have ever taught in one place: the accelerated learning systems, the writing frameworks, the mental models, and now the AI workflows that tie them all together.

Now, you can get 10 years of my teaching, timeless meta-skills, and more by becoming a founding member of this newsletter. One membership, $995, and everything I’ve built is yours. You can watch the videos or load all of the transcripts into your AI.

By joining this program, you get $500 off the Agentic Academy for Knowledge Work, and all of my evergreen classes ($8,000+ value, based on what each program cost on its own):

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• Access to Cozora (dozens of comprehensive AI skills). Cozora provides weekly classes with AI creators just like you. Each class focuses on members sharing their real workflows and meeting each other, so you can find collaborators and stay on the frontier of AI. ($900/year value)

• Recordings from last year of AI Thought Leader School, plus 30+ prompts. Every class recording from last year’s school, along with the prompt library that powers the workflows we teach. ($1,000/value)

That’s $8,000+ in programs, frameworks, and community for $995, plus the opportunity to get help moving everything you do into the agentic era at a $500 discount.

Become a Founding Member today, and let’s build your best work together. Just follow the four steps below:

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Step #1: Visit the Blockbuster Blueprint subscribe page

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Step #2: Select the Founding option when purchasing

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Once you become a founding member, visit the Subscriber Perks page, and you should be able to unlock the perk and get the discount code.

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More soon,
Michael

The paradigm shift to agentic AI is the most significant shift in knowledge work since the release of ChatGPT

While the chat paradigm facilitated a 1.5x productivity boost, the agentic paradigm has already facilitated a 20x productivity boost for the most pioneering AI companies, as I share in “I’m Not A Programmer” Will Be The New “I Can’t Read” In 5 Years.

And that productivity boost is increasing rapidly as Anthropic and OpenAI add new features at a rate I’ve never seen before.

For example…

#1. Top venture capitalist Marc Andreessen is seeing a 20x boost among his portfolio companies:

Source: Monitoring The Situation

“At our leading-edge companies, estimates are that the leading-edge programmers are 20X more productive than they were a year ago. It's the most dramatic increase in programmer productivity ever.”
—Marc Andreessen, co-founder of Netscape and a16z

#2. Successful entrepreneur and president of Y Combinator, Garry Tan, saw a 400x boost in his productivity:

Source: Y Combinator

“ It turns out that I was actually doing 400x the amount of code. Obviously, I wasn't writing. I was directing 15 agents at a time to do so.”
—Garry Tan, President, Y Combinator

#3. Anthropic just released a report that showed that their programmers are 8x more productive since 2024:

Bottom line:

The agentic paradigm is already delivering significant productivity improvements, and that number is going to grow.

Programming is the “canary in the coal mine” of knowledge work for two reasons:

1. Programmers have been the first to adopt agentic AI because that was one of the first fields where the AI companies focused on training their models.

2. More and more knowledge work tasks are being turned into code that can perform the task at human-level autonomously.

Yet, a shockingly small number of people have made the shift to the agentic paradigm:

Here’s the reason why so few people have made the switch…

Adopting agentic AI is 10x harder than adopting chat AI

What I’ve seen is that most people need support in order to make the switch, because in order to get value, they need to:

• Really understand its features

• Lay the proper foundation

• Create systems

• Think strategically about what they build

I’ll be more specific. Below is just a small sampling of what you need to learn to make the shift:

• Features: The 20+ essential agentic commands

• Planning: How to create plans that build the tool you want to create in one shot

• Error Correction: How to find the root cause of AI errors so they never happen again.

• Evals: How to create built-in checks so that AI can correct itself

• Knowledge Base: How to build an AI second brain with all of your data (transcripts, chats, etc)

• Context File: How to teach the agent who you are, how you work, and what you are working on

• Human In The Loop: Understand where you should be reviewing the AI’s work and where it can work autonomously

• Connectors: How to connect agents to external apps you use in your work

• Self-improve: How to make AI Agents learn and get better the more you use them

• Automation: How to build skills that automate your work end-to-end

• Security: How to safeguard your data from getting deleted, stolen, or distorted

• Strategic: How to decide what to build when you can build anything

• And more…

I personally love learning, and I had trouble making the shift on my own. It wasn’t until I hired a coach to teach me one-on-one that I really hit the ground running.

Upon realizing the importance of the agentic paradigm and the challenges to succeeding in it, many people have asked me what course I recommend.

I haven’t had a good answer.

That is, until now…

Announcing The Agentic Academy For Knowledge Work

I’m super excited to announce that I’m co-launching an Agentic paradigm program with , the creator of .

Wyndo and I have become friends over the last year, and I’m particularly excited to collaborate with him because:

• He runs the fastest-growing AI newsletter on Substack.

• Out of everyone I know, he’s the most proactive about experimenting and mastering new AI tools as they come.

• He’s a gifted explainer and teacher.

• Our styles complement each other.

The program launches on Monday, June 15, at 11:00am EST, and it will include:

• Community: WhatsApp community where all of your questions can be answered by Wyndo and me.

• Classes: 10 90-minute classes

• Mastermind: Monthly mastermind where Wyndo and I help you stay on top of the latest updates on how we’re creating things with agents.

• Skills: We’ve created self-installing skills that make it easy to transfer all of your data (LinkedIn, Substack, ChatGPT, Claude.ai) into your agent OS and then create a knowledge repo.

We’ll be sharing more about it next week, but for now, I just wanted to share a teaser so you can save the date.

Sign Up For Updates

We’ve created a new Substack publication to send updates if you’re interested in learning more.

To sign up for free, enter your email below:

It was called Augmented Awakening, and the focus was on using AI for deep self-understanding and transformation, rather than just productivity.

Today, I announce Augmented Awakening 2.0, which will happen every Tuesday at 11:00am-12:30pm EST starting June 9 and run for a year:

• June 9, 2026

• July 17, 2026

• August 4, 2026

• September 8, 2026

• October 13, 2026

• November 17, 2026

• December 8, 2026

• January 12, 2027

• February 9, 2027

• March 9, 2027

• April 13, 2027

• May 11, 2027

The live and on-demand classes and accompanying skills are only available to paying members of this newsletter. This means you get all of the benefits of this newsletter ($2,000+ value) in addition to this new class for just $20/month. To put this in context, Anand’s private coaching commands $1,000/hour.

Subscribe now

What’s New

This year’s cohort is going to take on a new flavor in two ways:

Change #1: From Simple Prompts To Complex Skills

First, rather than just sharing prompts, we’re going to share complex skills that can be used in chat tools (ChatGPT, Claude.ai) and agentic tools (Claude Cowork, Codex, Claude Code) that do way more. You can read more about Tuesday’s skill below.

Change #2: From Chat Paradigm To Agentic Paradigm

Second, we’re adapting to the new world of AI. Each session will focus on the aspects of awakening that are most threatened, or that offer growth opportunities that weren’t possible before.

About Anand

Anand is the most transformative coach that I’ve ever had the fortune to work with:

• Anand is incredible at understanding and reframing “problems” at a root level.

• He uses interventions that lead to effortless and elegant change in areas that seemed previously “impossible” to change.

• He uses a unique fusion of change work approaches you can’t get anywhere else (adult development, emotional regulation techniques, neurolinguistic programming, hypnosis, and other techniques he has pioneered).

• Once the basic life necessities are taken care of, personal growth is the highest leverage way to make the most out of our life IMHO. A small, deep change can have enormous effects in every area of our life.

Before becoming a full-time coach, Anand had a conventional technical background in theoretical physics and engineering. But then, he made a very unconventional career decision. Rather than maxing out his career in engineering, he focused on becoming a highly-paid consultant so he could work just a few hours per month.

This gave him extra time to make personal evolution his #1 focus. During this time, he sought out and learned directly from many of the world’s top change-work practitioners.

As a result of 30+ years of compounding, Anand now has the most distinctive and effective mix of change-work modalities I’ve ever seen. His approaches are a mix of:

• Mixed method (objective (science-backed) and subjective (intuitive))

• Multi-modal (intellectual and emotional and somatic and energetic)

• Multi-pedagogical (explanatory and experiential)

Normally, someone of Anand’s coaching level would be known across the world. But being widely known is not what drives him. Therefore, working with him now in intimate settings is a special opportunity.

Preview Of Next Tuesday’s Session (From Anand)

The Topic

You’ve probably noticed this already.

Building anything useful used to be the slow part.

Now you can start almost anything in an afternoon, point an agent at it, and go.

You handed it over in the morning, went off and did something else, and it worked away on its own. Good. That is new, and it is a kind of freedom.

Sit with it one more second, though. Feel your feet on the floor.

What were you really aiming it at? And under that, a stranger question. Was the thing you pointed it at actually yours, or just the one that was loudest that morning?

That second question is key.

Here’s why:

• How It Starts: When you can start anything in an afternoon, you start a great many things, and each one feels important when you begin it.

• How It Ends: You end up busy, productive even, and not at all sure any of it is the thing you would have chosen if you had stopped to choose. This can ultimately lead to burnout or, at the very least, overwhelm.

The building is handled now. The aiming is the part nobody is teaching.

So the skill that’s got lost in the mix is…

Prioritization

That is what next Tuesday is. It is the first session of a new run we are starting, and I wanted it to open right here, because this is the skill that sits underneath all the others.

What We’ll Do Together

We sit down and work, live, with the AI turned back on you as a mirror rather than an oracle. You lay your whole plate out, in no order at all, every project you have going or are tempted to start. And we use it to think clearly across the lot. Where the real reasons are. Where you are quietly telling yourself half a story. What a given thing would really cost you, in time and in the other things you would have to set down to do it.

You’ll leave holding the skill itself, a thing you keep and run before you build anything. It asks you what you would skip past, and it sorts the mess. And it keeps a quiet record of how you have been choosing, so each time you come back, it knows your patterns a little better. You get sharper at the one thing, choosing, by doing it.

One Caveat

I want to be straight about one part. It does not decide for you, and it will not save you from a hard call. All it does is make sure you are looking, across everything, before you set something loose. The deciding stays yours. That is the whole point of it.

Who It’s For

And you do not need to already be good at this to come. Most of us were never taught it, and the agents have just made it the thing that matters most. If you build with them every day, you will feel this sharply. If you have barely started, even better. You get to learn the aiming before the speed runs away with you.

The agents will build whatever you point them at. They are very good at building. Where you point them is the part that is still yours, and how you choose what to build turns out to be how you lead them. And how you lead yourself, for that matter.

So, before you point them anywhere, what kind of agent leader are you?

Bring your whole messy plate on Tuesday. Even the half-finished one you keep meaning to get back to. We will make sense of it together.

Tuesday, June 9, 11:00 to 12:30 EST.

See you in there.

Anand

PS - Michael wrote the section below to give more context on the agentic shift that’s going on and why Tuesday’s topic is particularly timely.

Five Ways The Agentic AI Shift Causes Impedes Personal Growth And Causes Overwhelm (From Michael)

AI makes us more productive.

So, you would think that this efficiency would give people more free time and more peace of mind.

But the opposite is actually true.

The people in the industry most impacted by AI are working harder than ever:

Steve Yegge, a legendary programmer who has held senior positions at Google and Amazon, takes us behind the scenes of what’s happening:

Source: The Programmatic Engineer

I believe there are five primary reasons why this is happening:

1. Abundance creates scarcity

2. Better AI leads to more work

3. The Red Queen Effect causes us to work harder and stay in the same place

4. Companies expect more

5. The New AI work requires more energy

By understanding what’s happening, you’ll be able to avoid the downsides of the coming productivity boom.

Reason #1: Abundance creates scarcity

You would think that abundance is only a good thing, but a hidden dynamic that few people realize is that many forms of abundance also create scarcity.

Most famously:

There are many other examples:

It’s now easier than ever to start a million projects and then be stuck managing them all.

Reason #2: Better AI leads to more work

As AI improves, it suddenly becomes possible to build way more things that weren’t economically feasible before. Thus, as AI improves, the universe of work increases. Aaron Levie, founder of Box (value: $3.7B), explains in an X post:

Sorry to anyone who thought AI would mean we’d work less (at least for now). AI makes it easy to explore more than you did before, and so you start doing far more as a result.

I regularly have seemingly small things that end up quickly consuming 3 hours because the agent made it easy to get started, but you still have to do the rest of the work to complete the project.

This is work that I wouldn’t previously have handed out to anyone else, it’s just stuff that never got done because it took too long to do fully manually. And, counterintuitively, for some of these tasks as AI gets good enough at doing them, it even becomes economically worth it to hire someone to do it on an ongoing basis with agents. But until you could try doing them at a low cost you would never have tried.

This is why AI won’t automatically reduce work in the way we imagine because work isn’t static. Most companies have far more they can do than they have today, it was just hard to get started on it all because of the natural constraints of time and labor availability.
—Aaron Levie on X

Reason #3: The Red Queen Effect causes us to work harder and stay in the same place

There’s a scene in Lewis Carroll’s Through the Looking-Glass where Alice is running as fast as she can alongside the Red Queen, yet the scenery never moves. She points out that back home, running that hard would get you somewhere. The Queen replies: “Now, here, you see, it takes all the running you can do, to keep in the same place.”

A century later, in 1973, the evolutionary biologist Leigh Van Valen turned that scene into a law. In his paper “A New Evolutionary Law,” he showed that species are locked in a permanent arms race with the species around them. A rabbit that evolves to run faster doesn’t stay safer for long, because the foxes evolve to run faster too. Everyone is improving, so improvement stops being an advantage. It becomes the price of staying alive. He called it the Red Queen Effect.

This is exactly what’s happening with AI, and it’s the part people miss when they picture AI handing everyone more free time.

When you were one of the first to use AI well, it was a real edge. You got more done than the people around you. But the trap is this: everyone else got the same tools at the same time. So the moment your 3x output becomes normal, it stops being an edge and quietly becomes the new baseline. You’re no longer running to get ahead. You’re running not to fall behind. And because the tools get better every few weeks, the baseline keeps moving, so the running never stops.

It’s the cruelest version of progress. The faster everyone goes, the harder you have to push just to stay in the same place.

And this dynamic doesn’t stay abstract. It shows up in a very concrete place: your job. Which brings me to the next reason.

Reason #4: Companies expect more

I’ve talked to countless employees one-on-one about AI. I almost universally hear the same thing. Leadership expects everyone to be more productive and to learn AI on their own. Rather than providing guidance, many leaders impose restrictions on what tools and data can be used.

Steve Yegge explains:

Source: The Programmatic Engineer

Reason #5: The new AI work requires more energy

Source: The Programmatic Engineer

Bottom Line

As AI helps us become more productive, we need to be more intentional.

If we aren't, we’ll end up running faster and stay in the same place.

Augmented Awakening 2.0 brings that intentionality to how we collaborate with AI.

All you have to do in order to join is become a paid subscriber of this newsletter for $20/month:

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Steps to AI Blockbusters

Most people using AI for writing are leaving enormous value on the table. They prompt once, accept the output, and move on. But the writers and thinkers who are pulling away from the pack aren’t doing that — they’re building systems: systems that encode their mental models, break down the creative process into deliberate steps, and use AI to explore a whole possibility space, not just confirm what they already think.

That’s what this session of Blockbuster Live is about. “Steps to AI Blockbusters” is one of the core sessions in my thought leadership course, where I teach serious writers, experts, and entrepreneurs how to produce ideas that travel — and how to build the AI-powered workflows that make it possible to do that consistently, at scale. If you’ve been wondering what separates AI-assisted content that’s forgettable from content that actually moves people, this is the session to watch.

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In 2025, context engineering blew up as a skill:

All at once, everyone realized that their AI gave dramatically better answers when provided with enough context.

In 2026, mental models is the opportunity.

With mental models, you can functionally upgrade your AI from Claude Opus 4.7 to Claude Mythos.

To understand how and why, you first need to understand three things:

1. Why more intelligent AI can be worth $30,000/month

2. How everyone can make their model signifantly smarter

#1. How More Intelligent AI Can Be Worth $30,000/Month

A few weeks ago, I was speaking at a mastermind of entrepreneurs with 7-8 figure businesses. I asked everyone how much they would pay per month for early access to Claude Mythos, Anthropic’s big new model that’s so powerful they couldn’t release it to the public.

I started the bidding at a few hundred dollars per month, and it went all the way up to $20,000 per month.

That’s how valuable intelligence is.

Here’s another way to think about it. I’ve been happily paying $200/month to access the latest models of Claude and be a heavy user. Theoretically, I could just use open source, local models, and pay less. They’re just six months behind. But I’d rather pay extra to be on the frontier.

Now that you understand how important intelligence is, it’s critical to realize that you, as an individual, can actually increase your AI's intelligence even if you’re not at a big lab….

#2. How Everyone Can Make Their Model Significantly Smarter

At first glance, it would seem that there is no way to make the AI we get from labs smarter. They have huge teams of the world’s smartest people with the biggest budgets all focus on making AI smart.

This is obviously not the case. Here are a few examples of research that shows that fairly simply interventions can have a big impact:

• Simply adding the words “let’s think step by step” lifted an older model’s score on a set of math word problems from 17.7% to 78.7%.

• Asking the same question several times and keeping the most common answer raised grade-school math accuracy from 56.5% to 74.4%, a jump of almost 18 points.

• Letting the model try several paths and back out of the dead ends instead of marching down one line took GPT-4 on the Game of 24 puzzle from 4% to 74%.

• Telling it to name the governing principle before solving the specific problem raised accuracy by 7 to 27 points across physics, chemistry, and multi-step reasoning.

• Letting the model select and combine its own reasoning moves for a task beat plain step-by-step reasoning by up to 32 points, and beat the sample-and-vote method above while using 10 to 40 times less computation.

• Giving it a library of reusable thinking templates to pull from improved results by 11% on one puzzle and 51% on another over the previous best.

• Handing it a small kit of named reasoning moves to run on demand lifted a standard model from 32% to 53% on a hard math benchmark, past a specialized reasoning model that costs far more.

• Giving a model tools instead of more size let a 6.7-billion-parameter model match models roughly 25 times larger once it could reach for a calculator and a search engine.

• Combining several weaker models so their blind spots cancel let a committee of open-source models score 65.1% on a standard benchmark, beating GPT-4o at 57.5%.

• Microsoft Research published a method, SkillOpt, that improves an AI by training the instruction document you hand it rather than the model itself. Freeze the model, optimize the words. It was best or tied-best on all 52 of its tests.

More recently, I created a mental model system prompt that makes your AI smarter, and I wrote about it here:

Whenever I talk to readers one-on-one, they point out how much better the mental model-enhanced response is compared to the generic AI response.

To understand why, you need to understand one of the simplest and most powerful mental models that almost no one knows about…

The Universal Intelligence Framework Explains The Power Of Mental Models

There is a simple 4-step formula that EVERY intelligent system in the universe has, whether it’s a human, a rabbit, or AI:

Here’s how it works for humans in a nutshell:

• We take in information. We humans take in information from direct experience, from consuming knowledge, and from other people.

• We process that information. We use a combination of conscious and unconscious algorithms to make sense of that data, connect it to other knowledge we have, and update our existing understanding of the world. Two people looking at the same data could process it in completely different ways.

• We experiment. After making sense of new information, we take action in the world. For example, after we read a book, we might get a few ideas of things we’d like to try. So we turn ideas into action items, experiments, and projects.

• We get feedback. As we take action, we receive feedback from our environment or our dashboards. Then, use that feedback to update how we go through the next loop.

Organisms that learn faster can perform each step more quickly and effectively.

This model is so powerful and simple that it served as the bedrock of an Accelerated Learning course I taught for years.

It’s particularly powerful in this moment, because it gives us four ways to make AI smarter…

The #1 Insight The Model Gave Me On How To Make AI Smarter

A. Information

Using this model, I was able to contextualize context engineering. Providing the right information to AI is just the first of four steps in the model.

B. Algorithms

Once AI takes in information, it has to make sense of it:

• Categorize it

• Prioritize it

• Contextualize it

• Sequence it

• Connect it to other knowledge

• Synthesize it

The algorithm stage is how the information gets processed. Two people (or two AIs) handed the same data can reach completely different conclusions depending on the algorithms running underneath.

Mental models are the algorithms. When you tell AI to think through second- and third-order effects, or to reason from first principles, or to apply the Pareto principle, you’re handing it a processing operation it likely wouldn’t have used by default.

C. Experimentation

After processing information, an intelligent system has to act on it and then see what happens. I call this experimentation rather than “action” because the point isn’t just to do something, it’s to test whether the ideas work and learn from the result.

Here’s what’s changing: we used to be limited to one shot. Write one article, ship it, wait for feedback. Now the rate of experimentation can jump by orders of magnitude. Instead of one version, you can have AI generate ten different approaches, take the learnings from each, choose the best, or synthesize a new best. One system I’m building inside Claude Code tests an article’s title and intro against real paid ads—running 30 titles past a lookalike audience to see which hook actually earns clicks before I commit to writing. The constraint was never the number of ideas; it was the cost of testing them. AI collapses that cost.

D. Feedback

The final step closes the loop: you take in what came back and use it to run the next cycle better. This is where evals matter most. The better you can define your goal, how to measure it, and a rubric AI can use to judge “did this work or not,” the more your system can iterate on its own until it reaches something good.

This is also the step where the deepest learning lives. Most people, when AI gives them a flawed output, just say “fix it” and move on—they change the single thing in front of them. But you can use that feedback to make bigger changes: not just what was wrong, but what thinking led to it, and how to update that thinking so the same error never recurs. That’s the difference between correcting an output and improving the system that produces outputs. Andrej Karpathy’s recently released Auto-research points at where this is heading—feedback loops tightening to the point where the system improves itself.

Session Overview

In this session, I:

• Go deeper on why mental models are the hidden multiplier inside every high-performing AI system.

• Give an overview of mental models

• Share Charlie Munger 5-step approach to using mental models

• Explain the challenges of using mental models without AI—you have to remember them, pick the right ones, and run them in the right sequence in the moment.

• Demonstrate how AI overcomes each challenge

This class is about the architecture underneath AI — and once you see it, you can’t unsee it. The gap between people using AI casually and people who have built mental-model-powered systems is widening fast. This session is about crossing that gap.

I built this class as part of my ongoing Blockbuster Live series, where each session covers one high-leverage idea at the frontier of AI and learning, and gives you something concrete you can apply immediately.

During this session, we:

• Explored why mental models are the hidden multiplier in AI productivity

• Traced the full prompt progression, from chat to skills to autonomous systems

• Walked through the three core ways to embed mental models into AI workflows

• Saw how mental models function as a “command language” for better thinking

• Examined Gary Tan’s GStack system, built on dozens of embedded mental models

• Live-demoed a system prompt that automatically applies mental models to every query

• Shared the G-Brain mental model encyclopedia and how to integrate it into Claude

• Discussed the learning loop — how AI improves not just output, but your own thinking

• Heard a live case study from a participant using fully autonomous AI systems

• Answered participant questions on debugging AI errors, system design, and skill-building
  

AI-Generated Podcast Summary Of The Class

How To Access The Full Course

Free members get a 30-minute video preview of the class.

Basic paid members get:

• Access to a monthly 90-minute class for 12 months.

• Prompt to create specialized profiles for any context

• Class resources (chat transcript, slides, full class transcript, prompts that are shared)

Said differently, paid members get access to 18 hours of learning for just $20/month or $149/year. This comes to just $8 for every hour of live class. And this doesn’t even include our other live monthly class, Augmented Awakening, or over $2,500 in other perks (20+ prompts, 7 courses, 3 books, blockbuster article library, etc).

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RECORDING RESOURCES

1. Presentation Slides

2. Blockbuster Live Prompts

3. Class Transcript

4. Other Classes In The Blockbuster Live Course

5. Resources Shared

6. AI Timestamps

7. AI Chapter Summaries

8. Chat Transcript

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This isn’t my usual article.

A few times a year, I come across an idea so big it changes my life. When that happens, I drop everything and go all out to make sure it lands with you the way it landed with me.

This is one of those.

What follows connects dots most people are missing. And, it takes you behind the scenes of a small group of knowledge workers at the frontier who are already 20x’ing their productivity.

This article went through 20+ drafts and is the culmination of 100+ hours of research. It’s years of tracking and thinking about AI condensed into one read.

Article

“Why are these people making the world so hard for me to live in? Everything worked fine before.”

My mom said this to me last year, and it sent a dagger into my heart. She is 74, retired, and was, I want to emphasize this, a computer engineer.

My mom is still the most independent-hearted person I’ve ever met, and I immediately knew what she was really saying. The shift was causing her to lose what she valued most: her independence.

The same pattern kept repeating:

• Her old device would break (phone, TV)

• She would get the new version, which was “smart” by default

• The setup and usage would be overwhelming

• She’d spend hours trying to figure out something that would take someone else minutes.

• Until eventually, she’d either give up or get help.

With each new device that went “smart” and each offline process that went online, her independence eroded.

She did not see this coming. Almost nobody who gets left behind ever does. But the world was becoming more and more alien to her, and it felt like there was nothing she could do about it.

I’m writing this article because the same thing is about to happen again, on a drastically faster timeline, to a much larger group of people. And I think there’s a real chance that you’re one of them.

In fact, a huge percentage of people are already wondering the same thing about AI that my mom wondered about technology:

Why is this small group of people in Silicon Valley creating something that will completely disrupt my life, my plans for the future, my local community (in the case of data centers), and my decades of expertise that I’ve gone into debt for?

The resistance isn’t coming from where you’d expect either. This time it’s not just retirees struggling with new interfaces. Graduating seniors are booing commencement speakers for telling them to embrace AI. The people who would normally be most excited about the future are the angriest about it:

I resonate with the backlash.

Within two decades, AI may be orders of magnitude smarter, faster, and cheaper than any human worker, and there will be far more of it than there are of us. The downside scenarios are real.

But none of us get to opt out of the world we live in.

The knowledge workers who don’t embrace AI will be left behind, and no one is coming to bail them out. Those who embrace it will see their productivity shoot up to previously unfathomable levels.

But anger and excitement have one thing in common: neither one tells you what to do next…

The Most Dangerous Career Advice Right Now Is "Figure Out AI"

Everyone agrees AI is transforming the world. Almost nobody agrees on what that actually means for anyone’s career.

A clear, hopeful future has been replaced by fog. Now it’s hard to know whether AI will take all of our jobs in five years or just keep making us more productive and creative for the foreseeable future.

Therefore, it’s hard to know exactly what to do now. It’s hard to know which AI skills will pay off for years, and which will be obsolete by the time you finish learning them.

Many people are falling into one of two camps:

• Opting out of staying on the AI frontier and burying their head in the sand.

• Flailing around trying to do everything. Staying on top of the latest tools, AI models, AI harnesses, prompting techniques, and industry news. Working harder than ever, but not sure if they’re making real progress.

This article is about clarity.

It provides you with the one AI skill and the one category of tools that are virtually guaranteed to deliver the biggest return for knowledge workers who apply them.

This clarity is critical because once you know what won’t change, you know what to invest in now and can be confident it will pay off.

Not only that, based on Harvard research and early results of people who are making the switch (more on this later), I can confidently say that the skill you’ll need to learn is one that you’ll actually enjoy doing.

And if you haven't started yet, that doesn't mean you're behind. You're in Stage 2 of a 5-stage pattern. The window is still open.

Very few have felt the true magnitude and speed of what’s happening, because we’re all inside it. It’s so ever-present that it’s invisible. To really see it, you have to step outside of it.

This article will help you take that step outside.

The Multi-Century Pattern That Reshaped Civilization Twice Is Running A Third Time

In 1700, saying “I can’t read” carried no stigma. By 1900, it was a serious liability.

In 1990, “I don’t use computers” was a defensible position. By 2015, it ended careers.

Today, “I’m not a programmer” is normal. By 2030, it will sound the way “I can’t read” sounded in 1900.

I’m not predicting this flippantly.

We’re inside the third run of a historical pattern that has already reshaped civilization two and a half times:

1. Once for reading and writing (text literacy)

2. Once for counting and calculating (numerical literacy)

3. Finally, for using and authoring software (software literacy)

Just as reading literacy spread for centuries before writing literacy did, software usage (digital literacy) spread for decades before software authorship became mainstream.

Today, knowledge workers use dozens of software apps on their phones, in their browsers, and on their desktops. Someone who can’t use software is essentially unemployable as a knowledge worker.

Starting in November 2025, when AI agents became able to reliably create working code, we entered the second stage of the third literacy—Software Authorship —in which domain experts turn what they know into running systems using plain English, with AI doing the technical work.

On the surface, Software Authorship doesn’t sound like a civilizational shift on the scale of reading or arithmetic. For 50 years, software has been a niche specialty: built by highly paid engineers, used passively by everyone else. Calling it the next universal literacy feels like a stretch at first.

It isn’t.

On the surface, Software Authorship doesn’t seem likely to have much impact on the average knowledge worker’s day-to-day work and career trajectory.

It will.

On the surface, Software Authorship feels like the type of shift that will take decades to run its course.

It will likely take 5-10 years. Maybe less.

The shift will create a new generation of economic winners and losers:

• Winners: On the one hand, the most advanced AI users creating software will be 100x, then 1,000x, and then 10,000x more productive than the average knowledge worker who lightly uses AI in chat. This is already happening, which I explain later in the article.

• Losers: On the other hand, many people will be left behind. Way more and way faster than in any previous technological shift.

I see the tsunami coming, and 99% of people don’t recognize what’s about to happen. As a lifelong educator, my mission is to help people make this shift as smoothly as possible.

On a personal level, I feel motivated by both the opportunity and the risk:

• Over the past few months, I’ve seen my productivity skyrocket higher than it’s been at any other point in my career.

• I fear being left behind on a visceral level because my mom isn’t the only person I’ve watched be left behind in the past, and it’s brutal…

What Happens To The People Who Sit This One Out

A 2005 comment from a dear mentor still sticks with me:

“I used to be good at computers in the 80s. I shouldn’t have let my skill slip. Don’t make the same mistake I made.”

Earlier in his career, my mentor decided to be more productive by delegating technical tasks to his employees rather than learning them himself. Until one day, he woke up and realized four harsh truths:

1. He was completely dependent on others for basic tasks.

2. He had become the kind of person who got the warm handshake at the front of the room and the knowing look behind his back among employees.

3. He was losing contracts to others because he wasn’t keeping up with the times.

4. He was so far behind that he couldn’t catch up.

I remember one moment in my early 20s when he asked me for help with a very basic tech task. It felt so obvious, I couldn’t help but smirk. He paused, closely examined my face, and then immediately ended the interaction. He never asked me for tech help again.

Looking back, I see his vulnerability in asking for help and his shame at my response. I wish I could’ve responded differently.

He never did catch up.

My mom and my mentor weren’t stupid or lazy. On the contrary, during their careers, they were each ambitious and successful. They just didn’t develop a key universal literacy when they had the chance. And they didn’t realize what they’d lost until it was too late.

It’s the boiling frog problem. The water heats one degree at a time. The frog never feels the moment when it should jump out, until the moment when it can’t. AI is doing the same thing to most knowledge workers. Each new headline is interesting but not alarming. Each week, it still feels okay to start later. The water just gets a little hotter.

Realizing this, I started doing the one thing my mom and my mentor didn’t. In March 2023, I made the decision to focus on studying and writing about AI full-time.

Then last winter, my news feeds blew up…

I Spent Twenty Years Convinced I Wasn’t A Programmer. I Was Wrong.

I saw the most luminary programmers stop writing code all at once:

Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now.
—Andrej Karpathy (former head of AI at Tesla), 40,000 likes

Pretty much 100% of our code is written by Claude Code + Opus 4.5. For me personally it has been 100% for two+ months now, I don’t even make small edits by hand. I shipped 22 PRs yesterday and 27 the day before, each one 100% written by Claude.
—Boris Cherny (head of Claude Code) on behalf of Anthropic’s team, 7,000 likes

programming always sucked. it was a requisite pain for ~everyone who wanted to manipulate computers into doing useful things and im glad it’s over. it’s amazing how quickly I’ve moved on and don’t miss even slightly. im resentful that computers didn’t always work this way. I 100%, I don’t write code anymore.
—roon (prominent OpenAI engineer), 6,800 likes

The era of humans writing code is over. Disturbing for those of us who identify as SWEs [software engineers], but no less true. That's not to say SWEs don't have work to do, but writing syntax directly is not it.
—Ryan Dahl (creator of Node.js), 20,000 likes

I also saw headlines like this:

Before, AI would get you 80% of the way there, but you still had to manually fix the last 20% of bugs. Suddenly, people were saying that AI was doing all of their coding.

I knew I should try building software.

I was even excited by the idea.

At the same time, it also filled me with dread because I had already failed before, over and over.

My mom was a computer programmer. She encouraged me to follow that path. So in my teens, I bought the website design books and learned HTML and Adobe Photoshop.

Emboldened by my progress, I bought more advanced programming books and took a computer science course at school. But that’s when I hit a wall. Every time I pushed past the basics, the same cycle started:

• I’d write something

• It wouldn’t work

• I’d spend the next three hours figuring out why

Fix one bug, start building again, hit another.

Most of my time went to debugging. It wasn’t fun.

There was no dramatic moment where I quit. I just gradually stopped trying and made a quiet decision about myself: I’m not a programmer.

After college, I tried the other route to creating software: hiring coders. I found an overseas development team and spent $40,000 over a year, working nights and weekends, building an app that let people track their goals.

I quickly learned what it feels like to depend entirely on someone else to build what’s in your head:

• Because of the 12-hour time zone difference, one miscommunication cost a full day.

• I couldn’t tell whether a fix should take an hour or a week.

• I had no way to tell whether the work was high quality.

It was like bringing your car to a mechanic when you don’t know how a car works. You hand over the keys, you pay the invoice, you hope you weren’t lied to, and you hope your car works when you get it back.

Ultimately, the app failed.

Eventually, I lost interest because the whole process killed everything that made the idea exciting.

By the time AI coding tools arrived, I’d seen the data: virtually every successful software company has a technical founder.

At the same time, I said to myself, “I’m not a programmer. I’m a writer.” I’d made peace with it.

So, when Claude Code launched in 2025, my first reaction was: even if AI writes 95% of the code, I don’t want to spend all my time fixing the other 5%. Even with a shorter learning curve, not worth it.

Then came December 2025, when I saw everyone saying that AI could do 100% of coding. Even though I was interested, I didn’t make time for it. I was busy.

What finally broke through was a friend who sat me down in January and said, “I think you can do this. Let me just show you.”

In one call, he walked me through the basics and suggested a few things to try. For the first time in twenty years, I felt like the “programming door” might not be completely closed.

I tried it. And it wasn’t what I expected.

For my first real project, I decided to create a mental model manual.

I had spent four years creating these manuals by hand for my Mental Model Club. Each one took roughly 50 hours of research, writing, and editing. To start, I downloaded all of the old manuals onto my computer. Then, I asked Claude Code to analyze the structure of each manual. Finally, I asked it to produce a new one in the same structure.

Within a few minutes, I had created a manual on the Second Order Effects mental model. On my very first attempt, it was shockingly close to what took me a month to create manually.

That’s when the lightbulb hit me.

Over the next week, I created 300 more manuals with AI. Same depth as when I did it by hand. But way faster. The numbers didn’t lie:

• Before AI: 192 weeks to create 48 manuals

• After AI: 1 week to create 300 manuals

That’s an astounding 1,200x multiplier. And it had only cost me $50 (on top of my $200/month subscription).

Then I asked Claude Code to build something more complex. And it did. But the things I had to fix were never the code. My time was spent on:

• Deciding what to build

• Planning it out

• Iterating with the AI

• Judging what “done” looked like

For the first time, building software required my expertise, not someone else’s.

And because I wasn’t trapped in debugging hell anymore, something unexpected happened: I was having fun. Not in a forced way. Genuine fun.

I described what I wanted in plain English, and Claude built it. I didn’t write a single line of code. I didn’t read a single line of code.

The systems I’d dreamed about for years, but never had a way to build, were suddenly real. And building them turned out to be the most direct path to everything I’d wanted to do with my work.

After I built one tool, I built another. Then five. Then 20. Then dozens more. In just a few months.

Every one of those tools encodes my expertise in ways that no software company would ever productize, because the knowledge is mine. A 27-step news analysis pipeline built on years of mental models I’ve developed. A writing voice system that captures my exact style. An AI-powered research system with over 12,000 notes searchable by meaning, not just keywords.

None of it required me to be a programmer. It just required my domain expertise and my AI prompting expertise.

After months of spending most of my day programming, I noticed two things that surprised me.

First, I realized I was no longer just a thought leader who happened to program part-time. I was actually a programmer. For example, to generate articles like this one, I spent most of my time developing software to streamline the process. Within a few months, I went from not knowing how to code to identifying as a software engineer. It has been the fastest identity shift I’ve ever gone through in my life.

Second, I realized that I actually love programming now…

The Harvard Research That Explains Why I Was Wrong About How I’d Feel About Programming

There’s a Harvard psychologist named Daniel Gilbert who studies something called affective forecasting: our ability to predict how we’ll feel about experiences we haven’t had yet. His finding, across decades of research, is that we’re terrible at it.

We consistently overestimate how much we’ll hate many things we’ve never tried.

Gilbert’s lab has shown this across romantic breakups, tenure denials, election losses, and dozens of other events people are sure they’ll never recover from. They almost always recover faster than they predicted.

This TED Talk clip summarizes the research:

And, according to a follow-up study, the single best predictor of how you’ll actually feel?

Asking people who’ve already done it.

In the study, Gilbert and his collaborators asked undergraduates to predict how much they would enjoy a 5-minute speed date and a peer evaluation.

• One group got detailed information about the event itself.

• The other group got just one stranger’s reaction to the same experience.

The strangers’ reactions won.

People who relied on a single secondhand report predicted their own feelings more accurately than people who studied the situation in detail and then predicted how they would feel.

And the kicker: when participants were given the choice, they preferred the detailed information. They actively rejected the strategy that worked.

This research is relevant right now because most of the people who move to coding with AI actually enjoy it. Boris Cherny, the creator of Claude Code, says that this is roughly what he sees at Anthropic among people who make the shift. And Anthropic is at the leading edge of this wave.

Furthermore, Lenny Ratchitsky, who interviewed Cherny, found something similar when he did three polls on X that collectively got 1,500+ responses:

Below are the specific poll results:

If Gilbert’s research holds and the trend continues, most people who switch to AI programming will enjoy it.

So the emotional barrier is probably lower than you think.

But there's a second barrier most people haven't questioned yet: the assumption that building software requires a different kind of thinking than you’re already using.

It doesn’t.

Your Job Is Already Software. You Just Don’t See It Yet.

Let me show you what I mean.

Strip away the job titles, and every knowledge worker is doing the same three-step loop all day:

• Input. Take in information.

• Transformation. Make sense of it, process it, create something.

• Output. Export the result.

For example:

• A lawyer takes in the details of the case, drafts the argument, and files the brief.

• A marketer takes in funnel data, develops the angle, and ships the campaign.

• An accountant records transactions, prepares the reconciliation, and sends the report.

• A designer takes in references, develops the direction, and ships the layout.

Every one of those workflows IS fundamentally like software. Information in. Transformation in the middle. Information out. The shape of every knowledge worker’s job is the shape of a software workflow.

If you’re a consultant or a coach or a strategist and you just felt your jaw tighten at the idea that your job is “fundamentally like software,” I get it. I had the same reaction. My work felt too human, too intuitive, too judgment-dependent to be described that way.

But only the shape of the work is software. The soul of the work is domain expertise.

Looking at the full sweep of knowledge work, four distinct eras emerge, each one inverting the relationship between human and software a little further:

1. Era 1: Human With Mechanical Tools (before ~1980). Work happens entirely in the human’s head and hands with mechanical tools. Paper, pens, ledgers, typewriters. Software doesn’t exist as a workplace tool.

2. Era 2: Human With Software (~1980 to present). The human is the agent. Software is the tool. The human does the work, and the software helps along the way. The lawyer types in Word. The accountant works in Excel. The marketer logs into HubSpot. (This is where most of us still are. We've spent our entire careers getting very, very good at being the human in "Human With Software.")

3. Era 3: Software With Human (2024 to present). The inversion happens. Software does the work. The human directs and judges. The founder doesn’t write the cold outreach. She designs a system that writes thousands of personalized messages while she sleeps. She isn’t using software the way her predecessors used Outlook. The software is doing the work. She’s directing it.

4. Era 4: Software-Only (~2030+). Software does the work autonomously without human direction in real time. Already true in narrow domains: algorithmic trading, automated support for routine cases, dynamic pricing engines, ad bidding. Likely to expand as Era 3 systems mature.

We are standing at the line between Era 2 and Era 3 right now. The professionals who have already crossed it are building 20x leverage. For example, serial entrepreneur Garry Tan is literally coding 400x faster than before AI:

Source: Y Combinator Podcast

Although Tan is an outlier because he’s an early adopter and world-class software engineer, he’s not alone. Legendary entrepreneur and investor Marc Andreessen reports that programmers in his portfolio company are 20x more productive with AI. This experience also aligns with my daily experience and that of friends who are all in on the tools.

I now believe that, in a year or two, people still operating with an Era 2 mindset (humans with software) will watch their work get done around them by people with a fraction of their experience.

The skill you need to move from Era 2 to Era 3 (software with humans) is Software Authorship: the ability to turn what you know into running software, using English as the interface and AI as the implementation. Not writing code. Just describing what you need, precisely enough that AI can code for you.

Boris Cherny is the head of Claude Code at Anthropic, the tool I use to build my own software. He stopped writing code entirely in November 2025 after AI became good enough to write it for him. But the most important thing he’s said isn’t about coding is about who builds the best software:

“The best person to write accounting software, I think maybe even today, is not an engineer. It’s a really good accountant because they know the domain really well. And coding is the easy part. It’s knowing the domain that’s the hard part.”

The technical part is now the easy part. The knowledge you’ve spent your career developing is the hard part. And “hard” here means “valuable.” It means “irreplaceable.” It means that the person who knows a domain most deeply builds the best tools, because the tools are made of domain knowledge now, not code.

This is why I’m saying that almost every knowledge job has software-shaped holes in it that better software would fill:

• The reports your team runs that take hours to compile.

• The data hand-offs between systems your IT department keeps promising to fix.

• The dashboards you wish you had.

• The custom tools that would make your job 40% easier, if only somebody would build them.

The only reason these tools don’t exist is because the supply of programmers has always been tiny relative to the demand for software.

That supply constraint just broke. Because now it’s possible for anyone to build.

The Five-Stage Pattern That Has Reshaped Civilization Twice Is Running Again

Everyone is asking, “Will AI replace me?”

That’s the wrong question for this moment.

The real question is bigger and older: what happens when a skill that only specialists have becomes something everyone can do?

That question has been answered exactly twice in human history. Both times, the answer unfolded as a five-stage pattern that reshaped economies, professions, and daily life. I call that pattern the Literacy Arc.

My mom and my mentor didn't see the Literacy Arc until it was too late. The rest of this article lays out the full pattern so you can see where you are right now and respond better: the five stages, the two forces driving them, the historical precedent, and the specific window that will be open for the next few years.

READ THE REST OF THE ARTICLE

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#1. The full article

The article is roughly 12,500+ words and includes 10+ videos and charts to make the ideas more concrete and visceral.

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This includes slides and an audio podcast you can listen to while walking or doing chores. Scroll to the bottom of this post to access them.

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Now let me show you the full pattern…

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From Research to Blockbuster Ideas: Using AI to Generate Transformative Thought Leadership

Most people think great thought leadership starts with writing. It doesn’t. It starts with ideas — and most people are generating ideas the wrong way.

They fixate on one idea too early. They skip the research. They jump straight from a vague prompt to an article and wonder why the output feels flat. What actually separates a piece that gets 92,000 shares from one that disappears is a disciplined, systematic process — and AI has made that process available to anyone willing to learn it.

This is what the Seminal course is built around: not just how to use AI to write faster, but how to use it to think better. To find ideas that are genuinely counterintuitive, that carry real intellectual weight, and that position you as a thought leader with a distinctive point of view — not just another voice adding to the noise.

In this session, we went deep on the bridge between research and id…

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AI-Powered Research for Blockbuster Content

Most writers treat research as a necessary chore — something you do to support an argument you’ve already decided to make. I’ve spent my career doing the opposite, and it’s been my single biggest competitive edge.

When I was writing regularly for Medium, I spent 80% of my time on research. Not to cherry-pick evidence, but to genuinely understand a topic well enough that original ideas became obvious. That’s a different thing entirely. It’s the difference between writing that sounds credible and writing that actually is credible — work that holds up, that readers return to, and that can’t be easily replicated by someone who just skimmed the surface.

What AI changes is the scale at which this is now possible. The research that used to take weeks can now be done in hours. More importantly, AI doesn’t just help you do research — it becomes the user of that research, able to synthesize and generate from a knowledge base that wou…

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Most people treat AI like a search engine with a chat interface. They paste in a question, get an answer, and move on. That approach will always hit a ceiling—because it puts you as the bottleneck, manually copying and pasting between screens, losing context, and starting over every session.

The Blockbuster Brain is a different paradigm entirely. It’s a living, queryable system built on your data—your podcast clips, your articles, your Zoom transcripts, your notes—that gets smarter the more you feed it, and can run complex operations autonomously while you sleep. In this class, I showed participants exactly how I’ve built this system for myself using Claude Code, including the mistakes I made spending 100+ hours on the wrong approach and how a single tool changed everything.

This is part of a broader course on using AI not just to work faster, but to think better. If you’re serious about staying ahead of how knowledge work is changing—and it is changing faster than most people realize—this is the work that matters.

During this class, we:

• Explored why traditional second brains fail and what makes AI different

• Walked through the full Blockbuster Brain architecture I use daily

• Compared AI-augmented learning vs. full automation—and the tradeoffs of each

• Saw a live demo of Claude Code building automations in real time

• Discussed how creativity actually works and why a broad knowledge base is the key input

• Learned why Claude Code is fundamentally different from chat-based AI tools

• Explored the “Third Brain” concept: a repository of cognitive operations, not just notes

• Looked at how to build a queryable knowledge layer on top of raw source files

• Covered how to extract hidden expertise from your own transcripts and conversations

• Answered participant questions about setup, tool choices, and where to start

Implications

The shift I’m describing isn’t just a productivity upgrade—it’s a structural change in what knowledge work even means. For most of history, the limiting factor in creating valuable ideas was access to information and the time to process it. Both constraints are dissolving faster than most institutions or individuals have reckoned with.

What this class points to is a world where the quality of your thinking is increasingly determined not by how hard you work, but by the architecture of the systems you’ve built around your mind. The people who understand this now—who take their accumulated knowledge, their frameworks, their hard-won expertise, and encode it into systems that can query, recombine, and surface insights on demand—are building a compounding advantage that will only grow.

The deeper implication is philosophical: creativity, as researchers like Robert Weisberg have documented, isn’t magic. It’s combinatorial. The broader and more diverse your knowledge base, the more raw material your mind—and now AI—has to work with. Building a Blockbuster Brain isn’t about outsourcing your thinking. It’s about expanding the surface area of what’s possible to think.Discussed the one action participants should take before the end of next week.

AI-Generated Podcast Summary Of The Class

How To Access The Full Course

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• Access to a monthly 90-minute class for 12 months.

• Prompt to create specialized profiles for any context

• Class resources (chat transcript, slides, full class transcript, prompts that are shared)

Said differently, paid members get access to 18 hours of learning for just $20/month or $149/year. This comes to just $8 for every hour of live class. And this doesn’t even include our other live monthly class, Augmented Awakening, or over $2,500 in other perks (20+ prompts, 7 courses, 3 books, blockbuster article library, etc).

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RECORDING RESOURCES

1. Presentation Slides

2. Blockbuster Live Prompts

3. Class Transcript

4. Other Classes In The Blockbuster Live Course

5. Resources Shared

6. AI Timestamps

7. AI Chapter Summaries

8. Chat Transcript

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Yet, my own teenage daughter thinks I’m on the wrong side of history.

She isn’t a Luddite either. She’s just looking around as:

• Her generation gets hollowed out by social media addiction

• The same tech industry promises it’ll save us with something more powerful.

• Stories of sexual deepfakes of women and children spread.

• Her peers use AI to cheat their way through school.

• Environmental damage from data centers is increasing.

• AI companies illegally steal the intellectual property of creatives.

• The future job market disappears faster than anyone is willing to admit.

My son isn’t angry like she is. He’s indifferent. The technology that’s reorganizing the global economy doesn’t seem worth his attention.

I assumed I’d talk them out of it.

And boy did I try.

I opined on the opportunities, the ease of getting started, and the importance of moving early.

But the more I talked, the more I realized I was the one being lectured. My daughter wasn’t repeating talking points. She was describing a world she lives in that I don’t.

What I now see is that they have grown up in a completely different reality from mine.

When I was a teenager, I read inspiring magazine cover stories about entrepreneurs like Steve Jobs, Richard Branson, Bill Gates, and Jeff Bezos.

These stories of young people making it big and changing the world inspired me to co-found a business.

My kids grew up with the inverse:

• Elon Musk’s rants on X.

• Allegations about Bill Gates and Jeffrey Epstein.

• A decade of tech in the form of social media rotting their friends’ attention spans.

• A generation of parents losing middle-class footing despite doing everything right.

• Mind-boggling wealth concentration:

For me, the arrow of the future pointed up. For my kids, it points into oblivion. A fast-changing alien future no one can see.

So I stopped trying to convince them and started trying to understand them. I spent the next several months reading the research on AI and young people.

What I found is worse than I expected. And it’s worse than almost anyone in my industry is willing to say out loud.

This essay is what I learned, why I think the people building AI are systematically refusing to look at it, and what my daughter saw that I didn’t…

My Kids Aren’t Alone

A quote from an April 2026 Gallup poll of 1,500 young people captures the situation:

“Gen Z’s sentiment toward AI has become significantly more negative…”

This table, based on the poll, shows the magnitude of the one-year sentiment shift:

These stats are astounding when you think about them…

• Young people are typically the most excited about new technologies. This generation isn’t.

• As AI gets better, you’d think young people would expect AI to make them more productive. They think the opposite.

• These effects could skyrocket as we’re still at the beginning of the AI explosion.

After doing a lot of research, I now believe two seemingly opposite ideas are both true:

1. The near future will be awesome for select young people. The 5% most entrepreneurial young people will do better than any previous generation. They will learn AI on their own and build bigger businesses faster. In a fascinating tweet, the CEO of Y Combinator, the largest startup accelerator in the world, recently shared that 3X more companies in their latest 3-month batch hit $1M in annualized revenue compared to the previous cohort.
   

2. The near future for most young people will suck. The 95% who aren’t entrepreneurial or going into blue-collar work will have a very tumultuous and uncertain beginning to their career. Many will graduate with record college debt and a cost of living that’s higher than ever. 64% will move back in with their parents.

These two groups seem to be completely talking past each other.

Every single AI podcast I listen to enthuses about the immense opportunities for young entrepreneurs. Sam Altman says there has never been a better time for a young person to start a business.

Yet, what is left unacknowledged is that only a minuscule percent of the population has the desire and inclination to actually start a successful business—even with entrepreneurship education. And, I should know because I spent most of my career in that field.

But before I jump deeper into the research, it’s critical to understand what’s at stake here for us all…

Why Young People And AI Matter

“The future is already here—it’s just not evenly distributed.”
—William Gibson

The future doesn’t happen overnight out of nowhere.

It happens gradually in the hidden fringes as small trends go mainstream.

Therefore, one of the most reliable ways to make educated predictions about the future is simply to find where the future is already happening.

Coding is the canary in the coal mine. It's the field AI has hit hardest and earliest, and the patterns showing up there now will spread across knowledge work within a year. Watch the canary, and you get a year's head start on your own field.

Similarly, young people are a canary generation.

They give us our first glimpse of how AI may impact all knowledge workers over the next 3-5 years.

More so, the implications go beyond economics into politics.

In 2010, complexity scientist Peter Turchin published a viral study in Nature. He used a mathematical model of state collapse based on historical data to predict that the United States and Western Europe were heading into a decade of political instability around 2020. He was right. And the variable he identified as most predictive wasn’t inequality, or polarization, or media fragmentation. It was the “overproduction of young elites.” He particularly made this point in his 2023 book, End Times:

“Overproduction of youth with advanced degrees has been the most significant factor driving societal upheavals, from the Revolutions of 1848 to the Arab Spring of 2011.”

Given these stakes, the research findings so far are particularly important to pay attention to…

The Research Is Already Here, and It’s Troubling

In August 2025, Erik Brynjolfsson, the Stanford economist, published what may be the most important empirical paper on AI and employment to date. The study, Canaries in the Coal Mine?, uses payroll data from ADP covering millions of workers across tens of thousands of firms.

Since AI tools became widespread, companies have sharply cut hiring of young knowledge workers:

While experienced workers in the same roles have been largely unaffected:

There’s one important caveat, though.

Brynjolfsson’s paper is careful to note that post-pandemic tech hiring corrections, interest rate changes, and the unwinding of 2021-2022 overhiring all played a role in the drop, too.

So he conducted a follow-up study that tested alternative explanations (interest rates, post-pandemic correction, tech overhiring) and found that the AI effect only grew stronger.

Bottom line:

AI is adding intense pressure to young knowledge workers.

Despite this pressure on an entire generation, there is something odd that’s making it easy to ignore…

The Quiet Crisis

This emerging crisis did not arrive in a televised crash, or a market panic, or a single announcement from a large company saying the old bargain was over. It has arrived more quietly:

• A job posting did not appear.

• An internship class got smaller.

• A manager decided not to hire a new person after another retired.

From inside, the situation feels stable. If you already have a good job, your paycheck arrives. Your team is busy. The economy appears healthy. But if you are trying to enter the workplace, the same condition feels like a wall.

A full hotel looks successful to the owner and hopeless to the person outside with a suitcase.

Young people are the ones with the suitcase.

Not only that, the crisis is quiet on another level, because of how the metrics are measured.

Let’s say a recent computer science grad applies for lots of programming jobs, but doesn’t get hired. As a result, she decides to work in retail while applying for more software roles.

The data says she is employed.

It doesn’t say she is an unemployed programmer.

Multiply her situation by hundreds of thousands of more young people.

Bottom line:

Our statistics are based on the jobs people have held. They’re much less good at measuring the jobs people were prevented from starting.

How AI Breaks The Bottom Rung Of The Career Ladder

When I first saw the Brynjolfsson data, I thought the story was just about AI replacing junior workers.

That’s how most people are framing it.

But there’s something more profound happening here.

Historically, new technologies have hit experienced workers hardest. A study from MIT Sloan and Northwestern’s Kellogg School that tracked technology displacement across occupations from 1981 to 2016 found that when new tools could perform tasks in place of workers, all affected workers suffered wage losses. Younger workers, with less invested in now-obsolete skills, typically adapted faster.

But Brynjolfsson found that experienced workers in the same AI-exposed occupations didn’t decline. They stayed stable, or grew. This moment is unique in tech history because AI is replacing beginners, not veterans. It’s amplifying experiential expertise.

And the implications for all of us are more alarming than most people realize.

Here’s how work used to function:

• Step #1: Junior workers supplied codified knowledge. From college, they knew the rules, methods, syntax, research process, spreadsheet techniques, case law search, and coding patterns. They weren’t yet wise, but they could produce useful first passes.
  

• Step #2: Junior workers gain judgment via experience. That usefulness bought them a seat inside companies where they could observe experienced people making real decisions. Over time, through that proximity, they acquired something much harder to teach: judgment.

Simple. Right?

Here’s the issue, though:

AI replaces the codified knowledge of young people. It can draft, summarize, classify, scaffold, translate, synthesize, generate options, and imitate formats.

AI magnifies those with experience. Senior workers know what questions should be asked. They know what answer is suspicious. They know when a technically correct solution fails socially, legally, operationally, or morally. They know which facts matter and which are decoration.

Brynjolfsson puts the situation plainly in an interview with Derek Thompson:

“LLMs learn from what’s written down and codified, like books, articles, Reddit, the internet. There’s overlap between what young workers learn in classrooms, like at Stanford, and what LLMs can replicate. Senior workers rely more on tacit knowledge, which is the tips and tricks of the trade that aren’t written down.”

That’s the mechanism. And it creates a reinforcing loop with no natural brake:

• Better AI.

• Senior employees become more productive with AI.

• Fewer juniors are needed.

• Fewer juniors are hired.

Bottom line:

AI isn’t replacing experienced workers. It’s replacing the training process that creates them.

At which point, a problematic situation activates.

Entry-level work was never just about production. It was the mechanism by which tacit knowledge was transferred across generations.

The senior engineer didn’t just produce output. He was also training the next generation. Therefore, when firms cut junior hiring, they’re cutting their own training function. They just don’t notice it yet, because the training was invisible. It was embedded in the work itself.

I have on multiple occasions produced output with AI that I would have previously estimated would take weeks of work. I am the direct beneficiary of the productivity amplification that feeds the loop. That’s exactly why I find it so alarming. Because I can feel, from the inside, how rational it is for every individual organization to do this. And how catastrophic it becomes when everyone does it simultaneously.

The market sees today’s productivity. It does not automatically price tomorrow’s missing apprenticeship.

We are removing the tasks that train judgment because AI can perform the tasks before judgment forms.

The senior employees who hold that judgment today will eventually retire. And when they do, we will discover what it costs to have skipped a generation of training.

Every Door Closed at Once

Previous automation waves were painful but more navigable.

• Factory automation closed manufacturing entry-level jobs, but opened IT, maintenance, and service positions.

• Computerization eliminated clerical roles but created software, networking, and digital marketing jobs.

In every case, one set of entry points closed while another set opened.

There was always somewhere else to go. The transition might have required retraining, relocating, or starting over at the bottom of a new, promising field. But at least the new field existed.

AI will certainly create new jobs. But AI is doing something no previous technology has ever done. It’s closing entry points across most types of knowledge work simultaneously.

The same technology that automates junior coding automates junior legal research. It automates junior financial analysis, junior marketing, junior journalism, junior consulting, and junior administration. The entry-level tasks across all of these fields share the same characteristics: they’re relatively well-defined, have clear success criteria, require broad but shallow knowledge, and produce outputs that can be evaluated by more experienced humans.

This is precisely the kind of work at which current AI excels.

No previous automation wave has ever moved this fast or hit this many industries at once, while there were so few new jobs to turn to.

And the cruelest part? The social contract around education is breaking at the same time.

For decades, the deal was simple:

1. Go to college

2. Acquire skills and credentials

3. Get a professional job

4. Pay off your loans

5. Build a life

That contract is embedded in every aspect of a child’s life:

• Parental expectations.

• High school guidance counseling.

• Student loan structures.

It is the entire architecture of how we organize the transition from adolescence to adulthood.

AI is breaking this contract in a very specific and damaging way. It simultaneously reduces the value of credentials while maintaining credential requirements and increasing credential costs.

A computer science degree means less as a hiring signal because AI can write functional code. But companies still require it, because hiring practices change slower than technology.

And right now, student debt in the United States exceeds $1.7 trillion. To put that in perspective: it’s more than the GDP of Australia. An entire wealthy nation’s worth of economic output, owed by young people who took on debt to buy access to a middle-class life.

Bottom line:

A 22-year-old graduating with a computer science degree and $87,000 in student debt is not a symbol of irresponsibility. She is a symbol of obedience.

She did the thing the culture asked of her. She chose the hard major. She listened when adults said the humanities were risky and technical skills were safe. She accepted debt because the debt was supposed to purchase access to the future.

That is what makes the story morally difficult. The advice was not stupid when it was given. In the world of 2018 or 2020 or even 2022, studying computer science looked like one of the most rational decisions a young person could make.

The world changed faster than the advice.

Now, instead of saying “Learn To Code”, the advice is “Learn AI.”

While I think this is good advice for professionals today, I’m not sure it’s the most practical advice for a 10-year-old. When that young person eventually graduates from college in 2038, AI might be doing almost all knowledge work faster and smarter than a human can even keep up with. 12 years in AI time is like 100 years in normal time.

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