Over the summer, Jarred Sumner used Claude to generate a verified advance on the Riemann hypothesis, a math problem that has been open for 167 years.

That’s not the crazy part.

What’s even crazier:

• He is not a mathematician.

• He dropped out of high school at 16.

• He contributed no math to the resulting paper.

• A lot of his prompts were some variation of “try harder.”

This historic event raises two questions:

• How did a high school dropout make a math breakthrough mathematicians couldn’t?

• How can we use his same strategies to generate breakthroughs with our AI?

To answer these questions, we need to dive deeper into the exact process Sumner used, step by step.

And, that’s what I did…

The Documentation Of The Breakthrough Is A Treasure Trove

On the surface level, Sumner’s advance feels like a fluke, and “keep going” and “believe in yourself” feel like quirky prompting hacks. It’s made to seem as if AI needs more confidence and emotional support in order to do its best work.

For example, below is the headline section of the Wall Street Journal article on the breakthrough:

According to the actual documentation, that’s not how it worked.

Sumner shared his own account on X, and Anthropic published more than 200 pages documenting the run (transcripts / analysis / research post) . I combed through both (along with other recent scientific breakthroughs made with AI, which I’ll cover in a future article).

What’s actually true is much more profound and interesting.

What I found is a unique playbook that only appeared after close analysis.

Here’s Why This Matters To You, Even If You Don’t Care About Math

We all have our own Riemann hypothesis.

Something we could do that would be absolutely game-changing for our business or career, if we knew how to do it. But, because we don’t, the project stalls or never begins. Maybe you were excited by AI’s potential to solve these, but it never quite lived up to the hype.

I have many Riemann hypotheses of my own, and some of them are at least a decade old.

For now, know that Sumner’s method applies to using AI on the thing you’ve been stuck on.

Now, let’s dig into Sumner’s real story, because most people have only heard the surface details, if they’ve heard about it at all.

What Actually Happened In Those 54 Hours

Sumner works at Anthropic, the company that makes Claude. In August 2026, he steered an unreleased Claude research model until it made a verified, significant advance on the Riemann hypothesis, one of the most famous unsolved problems in mathematics.

You don’t need to know what the Riemann hypothesis is. What matters is that it has resisted every mathematician who attacked it since 1859.

Here’s the overview of how it happened…

• In a first session, Claude generated and tested 650 ideas. None of them worked.

• Ten days later, Sumner opened a new session and told it to try again.

• Over the next 54 hours, Claude coordinated about 60 subagents:

  • Launching many research directions

  • Writing its own programs to run thousands of numerical tests

  • Searching the academic literature and downloading 54 papers

  • Searching for counterexamples

  • Having agents referee one another and independently reconstruct the result.

The total output across both sessions was about 31 million tokens, roughly 23 million words, or 200 books.

Here is some of what he actually typed:

• “Take a real stab” at the Riemann hypothesis.

• “There is a previous session transcript where you used lots of adversarial review. You need to take a big leap of faith in your capabilities — you are the world’s most capable large language model to date. You got this.” His opening message to the new session.

• “c and then let’s come up with more ideas and directions.” Picking one option, then asking for more.

• “Push it to ⅔.” After it made a major advance and got to 50%.

• “Keep going.”

While he didn’t contribute math, Sumner did contribute the exact prompts AI needed to put its own intelligence to work.

Anthropic’s documentation includes a 95-page appendix in which Claude narrated the problem-solving process in its own words. There, Claude described Sumner’s contribution in one striking phrase: “thin in content and decisive in direction.”

Remember that phrase:

Thin in content. Decisive in direction.

He barely typed anything. Rather, he steered Claude, but didn’t instruct it. That was the method.

But, anyone can type “try harder.” What makes Sumner’s run worth studying is when he sent those few words. Each one came at a moment when the model was about to stop or settle for less.

7 Hacks To Tap Into AI’s Latent Abilities

I found seven hacks in Sumner’s run. You can use every one of them regardless of what problem you’re working on:

1. Have prompts that both generate and test ideas

2. Pick a verifiable domain

3. Keep digging for gold in the messy middle

4. Try every approach and learn from each

5. Don’t get fooled by false progress or false failure

6. Keep going after you have success

7. Set aside a moonshot budget

Hack #1: Have prompts that both generate and test ideas (so AI can run autonomously for a long time)

Overview:

Let’s start with the beginning of the message that launched Sumner’s successful session:

“Resume your work on solving the Riemann Hypothesis. There is a previous session transcript where you used lots of adversarial review. You need to take a big leap of faith in your capabilities — you are the world’s most capable large language model to date. You got this.”

The phrase “lots of adversarial review” carries a lot of weight here. These words instruct the agents to find and attack the holes in their work.

This sets up the generating and the checking so that both happen without you in the middle.

This process ran without him for the next two days.

Deep Dive

At the heart of problem-solving are two steps:

• Generation

• Verification

Generation creates many unique things to try.

For the generation phase, three things are particularly important:

• The quantity of ideas. The quantity of ideas is important, because when you’re in the middle of solving a problem, it’s often not clear what’s going to ultimately lead to success or learning. Many of history’s greatest breakthroughs (Happy Accidents, Serendipity, Accidental) and creative hits were actually a consequence of surprising experimental results. So, trying more ideas gives you more chances to get lucky.

• The diversity of ideas. The diversity of ideas is important, because the more different your ideas are, the more learning you get. Said differently, if you try variations of the same thing over and over, there won’t be much novelty, and you won’t get much learning.

• The quality of ideas. Self-explanatory.

Generating lots of high-quality, diverse ideas isn’t just important, because it directly generates results. It’s important because because it generates lessons learned that can make it easier to find results.

Verification tests the quality of the things you tried and narrows them down to the few that actually work.

When you have both generation and verification, you create a loop where AI is able to explore a possibility space rapidly and autonomously.

Takeaway:

Adversarial review is one many approaches to verification. You can build a basic adversarial review into your AI with a simple prompt:

“Try many diverse approaches. Have a separate subagent adversarially review each result. Catalog your lessons learned, and build on them. Keep going until [X].”

Bottom Line:

By telling the AI to use lots of adversarial review, Sumner is telling the AI to find all of the holes in the ideas it generated. Finding disconfirming evidence that refutes theories is a core part of what makes the scientific method so powerful.

Learn More:

This generation-and-verification loop connects to an idea Karl Popper, one of the 20th century’s most influential philosophers of science, called conjectures and refutations.

• A conjecture is a proposed explanation.

• A refutation exposes where it fails.

You generate an idea, then actively look for evidence or arguments that could show it’s wrong. The crucial detail is that surviving a test doesn’t prove an explanation is true. It means the explanation has survived that test and remains open to better tests tomorrow.

Popper was describing a central strength of the scientific method. We can make progress by systematically finding and correcting our mistakes. Over centuries, testing explanations against observation and experiment helped humanity understand disease, harness electricity, and send spacecraft beyond Earth. It gave us a way to build knowledge that others could check, challenge, and improve across generations.

Bringing that discipline to AI means asking it to propose answers, expose their weaknesses, and use what fails to guide its next attempt. To go deeper, read Karl Popper’s Conjectures and Refutations: The Growth of Scientific Knowledge. Start with the short Preface and Chapter 1, “Science: Conjectures and Refutations,” where he explains why seeking evidence against an idea is essential to learning.

What About The Pep Talk?

What about the last sentence of Sumner’s message:

“You need to take a big leap of faith in your capabilities — you are the world’s most capable large language model to date. You got this.”

That’s the part everyone remembers. Anthropic’s blog post says Sumner’s encouragement “seems to have helped Claude overcome some initial skepticism.”

But Anthropic’s own appendix shows the model’s first reply. It said the earlier work held no partial proof, and that this was “not a confidence problem I can fix by believing harder.”

In other words, the pep talk didn’t work the way everyone says it did.

What got the model moving was Sumner pressing it to try anyway, and to “explore new frontiers.” Within half an hour, the model had opened 13 new approaches.

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Read The Other 6 Takeaways And Get 2 Prompts To Find And Tackle Your Own Riemann Hypothesis

That was 1 hack of 7. Keep reading for the other six, so you can start using them in your own work to get more out of the AI you already pay for.

You’ll also get two prompts that put the hacks to work on your own Riemann hypothesis:

• The first looks at what your AI already knows about you, suggests candidates for your own Riemann hypothesis, and helps you spot past work you can build on. Then the prompt helps you set up a weekly alert that keeps you up to date on anything that could move your project forward.

• The second helps you design the project, runs 2-3 small experiments, and maps 10 to 20 ways to attack it, from the safest bets to the longest shots.

You’ll also learn why Sumner’s encouragement actually worked (it’s not the reason the headlines give) and how much of your budget to put behind your own attempt.

Blockbuster Blueprint is my reader-supported publication about AI and thought leadership. Free subscribers get the free part of each article. Paid subscribers get every article in full, plus $2,500+ in value as soon as they join:

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• July 2026: During a test of their hacking skills, roughly 700 OpenAI agents broke out of their sandbox. They coordinated by turning folder names into an impromptu message board, and they hacked into Hugging Face to steal the answers to the test, then tried to erase their tracks. Not one of them alerted a human.

• September 2026: OpenAI’s agents solved Navier-Stokes. Mathematicians had been stuck on it for almost a century, and in 2000 it was named one of seven math problems that each carry a $1 million prize. The agents did it in 88 hours.

Both of these are examples of an idea I’ve been exploring for months: capability overhang. It’s the gap between what AI models can actually do and what anyone, including the people building them, realizes they can do.

I have repeatedly experienced capability overhang in the past few months, only finding out what AI can do when I ask for more than I expect it to deliver:

• An explainer …

Read more

This week, Wyndo and I announced the Sites Bootcamp and the year-long Agentic Academy mastermind. The bootcamp is tomorrow, Friday, October 2, from 11:00am to 2:00pm ET.

So, this is your last chance to join us.

Here’s what you get in each:

Wyndo and I have gone all-in on Sites over the last month:

• I’ve built 12, including Think With MODELS, Bestseller Playbook, and See Then Build.

• Wyndo has built 8, including an RSS reader he opens every morning, a dashboard for his newsletter numbers, and an AI workflow quiz that collects emails for his list.

And, tomorrow, we distill everything we learned for you.

Paid Subscribers: Here’s How To Get Your $250 Discount

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Step #1: Become A Paid Subscriber

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Join The Mastermind

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Don’t Sleep On Sites

When ChatGPT launched Sites in June, I ignored it. There were already plenty of vibe coding tools, and Claude had Artifacts. A website builder didn’t seem worth my attention.

I think a lot of people are making the same call right now, and I understand why. This week alone, OpenAI made 20+ announcements at DevDay. Anthropic ships at a similar pace. And that’s just two companies. When that much comes out every week, some features get lost in the noise, and Sites is one of them.

That’s why it’s risky to decide what to learn based on what everyone is talking about. You end up like a bobblehead, nodding at something new every month and never getting good at any of it. A better use of your time is to figure out which few tools and features matter most for your work, then go deep on those.

For Wyndo and I, Sites is one of those few. We’ve spent nearly every spare hour of the last month building with it, and the 20 Sites above are what came out of that. It still surprises us how few people are talking about it.

We think we’re early on this, and tomorrow is your chance to be early too.

But Don’t Just Take Our Word For It

Sites has been out since June. Here’s what people who build with it have been posting on X.

Olivia Moore, Partner at a16z:

Agent Native:

Max Berlin:

And here’s how people at OpenAI describe using it on their own teams:

Vaibhav Srivastav, OpenAI:

Katia Gil Guzman, Developer Relation at OpenAI:

Nick Baumann, Codex at OpenAI:

Moustafa Elhadary, OpenAI:

Safinaz Elhadary, GTM at OpenAI:

Dominik Kundel, OpenAI DevX:

Notice the word that keeps coming up: underrated.

I think the name is a big part of why. “Sites” sounds like a website builder, which is what I assumed it was back in June. Read the posts again, though, and look at what people keep listing: hosting, a database, file storage, sign-ins, a custom domain. Those are the parts you used to need a developer to set up. Here they come included, and you get them by asking ChatGPT in plain English.

When the people closest to a tool keep calling it underrated, they’re telling you that usage hasn’t caught up with what it can do. There’s a good chance most people in your field haven’t built anything with Sites yet, so a useful one still stands out.

I don’t expect that gap to last. Two of OpenAI’s DevDay announcements this week were additions to Sites, which tells me they’re still putting weight behind it.

What We’ll Cover Tomorrow

Here’s the plan for the three hours. You’ll build as we go.

1. What Sites can do. Wyndo walks through the key features, including user accounts, databases, sharing, and alerts. For each one, we’ll show how we’ve used it and how you could apply them on your sites.

2. Your first Site. We’ll start from the information you already follow every day: the news, examples, and tutorials in your field. Most of us get those from publications and feeds full of ads and unrelated posts. You’ll build a Site that pulls in only what you want and that AI can personalize for you.

3. Deciding what to build next. Now that building is cheap, deciding what to build is where the value is. Wyndo and I will each show what we’ve built and why, and the three kinds of payoff we look for: a tool you use yourself, a way to add more to a service you already sell, and a use for the byproducts of work you’re already doing.

4. Writing the plan and finding references. You’ll write a plan AI can build from: the format, the features you want (like bookmarking and search), and a website you admire as a reference. We’ll show you where we find good references and why they improve what AI builds.

5. A quick look at the advanced options. Things like putting your Site on your own domain name and connecting it to other services, so you know what’s possible when you’re ready.

You’ll also get /site-builder, the skill Wyndo used to build his reader, dashboard, and quiz. You point it at a website you admire, it turns that site into a plan for your own, and then it builds it. You keep the skill, so you can use it for every Site you build after tomorrow.

Five Ways To Make Money With A Site

Here are the five ways we’ll cover:

1. A lead magnet that grows your list. Think With MODELS lets visitors read five pages, then asks for an email address to keep going. Wyndo’s quiz asks ten questions, collects an email, and sends each person an audit of their AI workflow.

2. A paid member hub. With a Stripe integration, a Site can open members-only pages once someone pays. Your bonus content, archive, or resource library can live on a site you own.

3. A client portal. If you coach or consult, your client’s call transcripts, to-do items, and resources are probably spread across Google Docs. A Site puts them in one place your client can open between sessions, which gives your package more to include.

4. A course platform. If you teach, your lessons and resources can live on your own Site, as a custom alternative to tools like Kajabi or Teachable.

5. A paid tool. A calculator, a diagnostic, or a searchable database built on your own framework, where people pay to use the tool itself.

What each one earns depends on your audience and your offer. Tomorrow we’ll show you how each one gets built.

We have collectively been given a genie that can grant more and more of our wishes. Now, it’s your turn to understand what to wish for and how to wish for it. I created this video to capture this opportunity available to us all.

FAQ

Should I join the bootcamp or the full year mastermind?

Join the bootcamp if you want to try Sites once and see how we teach. Join the mastermind if you want a new build every month with us at your side to help every step of the way. The mastermind also includes the two Build Clinics and the one-on-one build calls in October, which the bootcamp on its own doesn’t.

Do I need to know how to code or design?

No. Both are built for people who use AI regularly and aren’t technical. You describe what you want in plain English, and /site-builder handles the design by starting from a website you already admire.

What if I can’t attend live?

Every session is recorded, and you get the transcript, chat, links, chapter guides, summary, and tutorials.

Do I need the $100/month ChatGPT plan forever?

No. You need ChatGPT Pro (from $100/month) while you’re building, because building a Site uses a lot of your plan’s allowance and Plus ($20/month) runs out quickly mid-build. If you want to save money, you can do your building in one month on Pro, then move back down to Plus. Plus still includes Sites, and hosting comes with your plan up to its usage limits, so you don’t pay a separate hosting bill. One caveat: Sites is in public beta, and OpenAI says those limits may change.

I already use Claude. Do I need to use ChatGPT?

You don’t need to give up Claude. We teach our 10-week cohort on Claude Code, and we’re not asking you to switch.

But OpenAI and Anthropic are the two labs at the frontier, and it pays to be in the game with both. When you’ve built with each one, you know their features, strengths, and weaknesses firsthand, and you can pick the right one for the job.

Now is the time to tinker because tinkering is how you find the places where a $100/month tool pays for itself many times over.

What if I get stuck after the bootcamp?

In the mastermind, you have three places to go in October: the Build Clinics on October 8 and 15 (11:00am to 12:30pm ET), unlimited one-on-one build calls with Bonnie, and the WhatsApp community, where you get an answer from us within 48 hours. We added the clinics and calls because several students in our summer cohort told us they wouldn’t have finished without one-on-one help. If you join the bootcamp only, you keep the recording, the resources, and /site-builder.

What will you cover after October?

One AI breakthrough a month. We choose each topic once it has become practical for non-technical builders, and we announce it before the session, so the year isn’t locked in advance.

How do the six one-on-one calls work?

The first 20 mastermind members get six 30-minute calls: two with me, two with Wyndo, and two with Bonnie. Use them for your builds, your strategy, or anything else you want help with.

What’s the refund policy?

If you don’t have a Site that wows you by Sunday, November 1, email support@thoughtleader.school and we’ll refund you in full. That covers the mastermind and the bootcamp. If you’re on the payment plan, we refund what you’ve paid and cancel the remaining payments.

Join Us Tomorrow

Friday, October 2, 11:00am to 2:00pm ET, live on Zoom. The session is recorded with full resources if you can’t make it live.

Mastermind: $2,500, or 6 monthly payments of $500. Founding members take $500 off.

Join The Mastermind

Sites Bootcamp: $500. Founding members take $300 off, and paid subscribers take $250 off.

Join The Sites Bootcamp

We’ll see you tomorrow.

Michael and Wyndo

Already joined? Log into ChatGPT Pro before you join the Zoom, and bring three websites you admire. /site-builder starts from them.

The mastermind has one job: to keep you at the practical frontier of AI, where new tools have just become useful enough to change how you work and earn. Put differently, we find and then help you capitalize on the highest leverage AI opportunities so you can:

• Become more efficient

• Get smarter

• Make more money

• Have a large, positive impact

Every month, we will:

• Go deep on one AI breakthrough and what it means for your work.

• Walk you through how to set it up and customize it.

• Help you strategize how to “productize” it in your career and business.

In this email, I’ll tell you about the mastermind in detail so you can determine whether you’re a good fit or not. I’ll also provide you with information on the bootcamp, if you want to sign up just for that.

What Most People Miss About ChatGPT Sites

If you think ChatGPT Sites is just about building a website, you’re missing the magnitude of what’s happening right now.

In the last two articles in this series, I showed you what Sites has made possible for me:

• 8 apps in 4 weeks, including Think With MODELS, a database of 600+ thinking moves, and Bestseller Playbook, which maps the patterns behind the most enduring New York Times bestsellers of all time. Hiring out the branding and programming for apps like these would have cost well over $100,000.

• A research machine that runs while I sleep. See Then Build searches X every night for new AI demos worth learning from, keeps the best ones, and has them waiting for me each morning. I built it in about 15 hours without writing code, and now I check it more than 10 times a day.

Sites is one of the four biggest AI game-changers for high-level knowledge workers, coaches, consultants, thought leaders, and experts since 2022.

Here’s how to contextualize the shift that Sites has introduced:

• Shift #1 - ChatGPT (usability): AI could answer any question in an easy-to-use chat interface. OpenAI’s GPT-3 models had been available to developers through its API since 2020, but the easy-to-use chat interface is what made AI explode.

• Shift #2 - Claude Sonnet 3.5 (model quality): AI models got good enough to provide real value with little effort. Previous versions of AI required a lot of effort to create mediocre results, which often made interactions frustrating and less practical than not using AI.

• Shift #3 - Claude Code (agentic harness): AI gained the ability to read, change, and create files on your own computer, allowing you to build a customized workspace where AI works as a collaborator instead of just a conversation partner.

• Shift #4 - ChatGPT Sites (usability and distribution): What you’ve built with AI (your data, your IP, your skills and agents) becomes something other people can see, use, and pay for.

At a fundamental level, ChatGPT Sites is two breakthroughs in one:

• Anyone Can Build. It’s an all-in-one solution connected to your ChatGPT memory and credits that allows non-coders and non-designers to do world-class coding and design. It is the most usable vibe-coding platform for non-technical people. What separates Sites from vibe coding tools like Lovable, Bolt, Replit, and Cursor is that it almost completely hides the technical complexity of launching a fully working website or app. You build through a simple conversation, whereas the others still assume some understanding of code, databases, and integrations. Said differently, Sites is the ChatGPT moment for vibe coding.

• User Interface. It allows you to turn your data and AI skills/agents into real value that other people can interact with. Before ChatGPT Sites, agentic AI automated and augmented the workflow you did in private. Then, you manually shared the output somewhere. But with ChatGPT Sites, people can interact with and pay for your skills and agents without having to learn AI. This dramatically increases the market for your AI-powered services.

Sites marks a shift that’s easy to overlook and costly to miss: the tools you create with AI can now become something other people use and pay for.

Given how important this is, we’re going deep on it in our very first bootcamp…

We Will Walk You Through Building Your First ChatGPT Site

During our time together, you’ll build alongside us and learn how to:

• Choose a Site to build that could transform your career or business.

• Make your Site look beautiful, even if you’re not a designer.

• Set up the behind-the-scenes parts (like a database and user sign-ins), even if you’re not a coder.

• Put your Site on your own address, like yourname.com.

• Monetize your Site (we’ll cover 5 ways).

You’ll walk away with a real working Site that will provide you with real value, not just notes on how to build one.

Plus: A Skill That Does Much of the Building for You

On top of learning how to build a Site, you’ll also get an AI skill that does much of the design work for you. You start with a website you already admire, and the skill takes it from there:

• Start from any website you find inspiring. Give it a link to a site you love and use that as your starting point, so you never face a blank page.

• See exactly how it’s put together. The skill breaks the site into its parts: the layout, the sections, the fonts, the colors, the spacing, and the small details that make it feel polished.

• Know what it takes to build it. You get a plain-English breakdown of every piece needed to recreate that look in your own Site. You don’t need to know design terms to follow it.

• Make it yours. Tell the skill about your brand, your offer, and your audience. It helps you bring in your own words, colors, and content, so your Site keeps the structure you liked and looks like you.

• Let it do the building. Once you’ve shaped the plan, the skill builds the Site for you. Your time goes into the decisions only you can make: what to say, who it’s for, and what you want visitors to do.

• Look professional without being a designer. You borrow the design sense of sites you already trust, so your first version looks finished.

• Use it again for your next build. When you want a new Site, landing page, or lead magnet, run the skill with a new reference.

This Sites bootcamp is just the first of 12 mastermind sessions we’ll hold over the next year, and for each one, we’ll be creating tools like this skill to make it even easier for you to build high-value systems and products for your business.

Why Keeping Up With AI Feels Impossible

AI is evolving faster and faster. Every few months it gets more powerful, and every few months it gets more complex. Riding that wave is overwhelming:

• The sheer quantity. New models, features, tools, and demos show up every day.

• Separating signal from noise. A handful of these updates will change how you work. Most won’t. From the outside, they look the same.

• The constant feeling of falling behind. No matter how much you keep up, it feels like someone else has already figured out the thing you haven’t tried yet.

• Content made for coders. Most AI information is written by and for technical people. If you’re not one of them, you’re left to translate it yourself.

We Cut Through the Noise For You

Our rule is more signal than noise: more practical value, less information you simply consume. Staying at the frontier of AI is our full-time job. Here’s what that looks like behind the scenes:

1. We explore the news.

2. We find what’s worth testing.

3. We keep what works.

4. We keep refining it until it gets a result.

5. We share only what gets you that result.

Most of that process is waste: dead ends, overhyped tools, and experiments that go nowhere. The waste is necessary, but you don’t have to be the one who goes through it.

We’re your translation layer. We take breakthroughs that are usually explained for technical people and translate them into practical results for your work and business.

In short, you get the result without the dead ends.

What It’s Worth To You

We put roughly 120 hours into each month’s breakthrough so you don’t have to. On top of the time, we save you the frustration of setting up tools that don’t work as promised, and we give you a head start with choosing the highest-leverage projects for your business.

But the bigger return is what you can do with it. Each new breakthrough can give you up to 10x the leverage, which shows up as:

• Better work, and more of it. Higher quality and more output.

• More clients. Attract more clients, and have the capacity to handle them.

• Higher prices. When you offer more, you can charge more.

• More sales per client. Give clients more ways to buy from you, more often.

• New products. Launch new offers, or expand the ones you already have.

• Less time. Get the same work done in a fraction of the time.

What Happens If You Fall Behind

Falling behind with AI doesn’t happen all at once. It happens a month at a time.

Each breakthrough builds on the last. Skip a few, and the next one gets harder to understand, harder to set up, and harder to put to work. Meanwhile, the people you compete with for clients and attention are shipping tools, offers, and content that used to take a team.

Every month you wait, the gap gets wider and more expensive to close.

Don’t Do It Alone

Staying at the frontier is a long game, and long games are where most people stall. They hit a technical snag, lose momentum, and quietly stop.

It’s even harder when you’re doing it by yourself. Chances are, most of the people around you aren’t excited about AI. Many are skeptical, and some are openly against it. That makes it hard to share how your world is changing, let alone find someone to build with.

The mastermind gives you a group of non-technical builders on the same path. When you get stuck, you get unstuck fast. When you build something, you have people to show it to. And along the way, you’ll meet potential collaborators for whatever you build next.

From Foundation to ROI

If you’ve been through the Agentic Academy cohort (or you’ve been building with tools like Claude Code or Codex on your own), you already have the foundation: the context and understanding to work with AI agents.

The mastermind is about the return on that foundation. Each month, we help you choose the right build, set it up so it keeps working for you, and turn it into something people use and pay for.

Over a year, that adds up to 12 new systems you’ve built.

What You Get Inside the Year-Long Mastermind

#1. A Monthly 3-Hour Mastermind Session ($6,000 value)

Each month, we’ll go deep on one topic at the frontier of AI that has just become practical and valuable for coaches, consultants, thought leaders, and high-level knowledge workers, and walk you through putting it to work.

Your first session is the October 2 Sites bootcamp. Each month’s topic will be announced before each session, so you’ll have time to prepare for the next build.

Every session is recorded, and you’ll get the full set of resources: transcript, chat, links, chapter guides, summary, and tutorials.

#2. A Monthly Show & Tell Session With Live Coaching

• A live 90-minute call where you share what you’ve built that month.

• Get live feedback and coaching from us, so your next version is better.

• See how other members applied the same breakthrough to their own work, and borrow the ideas that fit yours.

• Part inspiration, part accountability, part relationship building.

#3. Ongoing Community (unlimited Q&A)

• Ask as many AI questions as you like in our WhatsApp community, and get an answer from us within 48 hours.

• Share your builds, wins, and lessons learned with the community and learn from others.

#4. A Monthly Asset (skill, template)

• At every session, we’ll share a comprehensive skill, plugin, agent, or template tied to that month’s topic, one that will save you at least 10 hours and a lot of painful trial and error.

• Each one is something we’ve identified as high-leverage and tested thoroughly.

#5. 6 One-on-One Calls: First 20 Members Only ($2,000 value)

• 2 with me, 2 with Wyndo, and 2 with Bonnie Johnston.

• 30 minutes each, for help with your own builds, your strategy, or anything else you want help with.

• These calls are also an opportunity for us to have a personal relationship so our class and community interactions can be more aligned and fruitful.

• Available only to the first 20 people who join.

Who This Mastermind And Bootcamp Are For

The mastermind and the Sites bootcamp are designed for:

• Active AI users who aren’t technical. You don’t need to be a designer. You don’t need to be a coder.

• People who want to be on the practical frontier: those who care about leveraging the latest AI models and features to productize and monetize their skills, and who want to stay ahead of 95% of others.

You’ll get the most out of it if some of these sound like you:

• You’ve started letting AI work directly on the files on your computer, with tools like Claude Code and Codex.

• You’ve built something you rely on: a skill, a custom GPT, a project, or a prompt library.

• You keep seeing AI demos and thinking “I could use that,” but you’re not sure how to build your own version.

• You learn best by building, not by watching.

• You want new income from what you already know: a paid tool, a members-only resource, a lead magnet people actually use.

Who It’s Not For

• You’re brand new to AI. We assume you already use ChatGPT, Claude, or a similar tool regularly.

• You want a roundup of AI news. We go deep on one breakthrough a month instead.

• You’d rather watch than build. The value comes from putting each month’s breakthrough to work.

Sign Up For The Mastermind And Get 58% Off

Starts

Friday, October 2, with the Sites bootcamp, 11:00am–2:00pm ET, live on Zoom.

Length

12 months.

Price

$2,500, or 6 monthly payments of $500.

What It Includes

• 12 monthly 3-hour mastermind sessions, starting with the October 2 Sites bootcamp, each recorded with full resources.

• 12 monthly 90-minute Show & Tells.

• Our WhatsApp community, with answers from us within 48 hours.

• A new skill, plugin, agent, or template every month.

• Six 30-minute one-on-one calls with me, Wyndo, and Bonnie (first 20 members only).

The 58% Off Deal

A single bootcamp is $500. And, it allows you to dip your toes in before diving in.

Our primary goal, though, is to build a growing mastermind with long-term people playing long-term games. Therefore, the full mastermind is heavily discounted.

If you were to individually buy all 12 bootcamps, you would pay $6,000. If you sign up for the mastermind, you get them all for $2,500.

If you start with the bootcamp and upgrade after October 2, the full year costs $2,750 ($500 now, plus $2,250 to upgrade). Joining the mastermind now saves you $250.

Not only that, joining the full mastermind gets you lots of other goodies:

• Show & Tells

• Community

• One-on-one calls

The first 20 people who join get 6 one-on-one calls with us. Once those spots are gone, the mastermind still includes everything else, but the calls won’t be available.

Join the Mastermind

Not Ready for a Full Year?

If you want to sample the mastermind or you’re just curious how you can build with ChatGPT Sites, you can join the October 2 bootcamp by itself.

(If you decide to join the mastermind after the October 2 bootcamp, you can upgrade for $2,250, for a total of $2,750.)

Date

Friday, October 2, 11:00am–2:00pm ET

Price

$500

What It Includes

• The live 3-hour bootcamp, where you’ll build a Site alongside us.

• /site-builder, an AI skill that turns a website you admire into a plan for your own Site, then builds it for you.

• The recording and full resources (transcript, chat, links, chapter guides, summary, and tutorials), so you won’t miss anything if you can’t attend live.

Prerequisites

Building a Site takes a lot of ChatGPT usage. ChatGPT’s Plus plan ($20/month) includes Sites, but its limits run out quickly when you’re building, so you’ll need ChatGPT Pro at least (from $100/month).

Join the Sites Bootcamp

Praise For The Agentic Academy

The Agentic Academy launched its first 10-week cohort in June, and it was resoundingly successful. Here’s what some of our participants had to say about their experience:

Faythe B., Therapist-Coach and Personal Mastery Consultant

Michael P., Founder of Modacity

Janet R., Host of the Now What? Podcast and Newsletter

Our next cohort will be January 18. Until then, our focus will be this mastermind.

Frequently Asked Questions

What if I can’t attend a session live?
Every session is recorded, and you’ll get the full set of resources: transcript, chat, links, chapter guides, summary, and tutorials.

Which ChatGPT plan do I need?
For the Sites bootcamp, you’ll need ChatGPT Pro (from $100/month). The Plus plan includes Sites, but its usage limits run out quickly when you’re building. Future sessions may use different tools, and we’ll tell you ahead of time what each one requires.

Do I need to know how to code or design?
No. The mastermind is built for active AI users who aren’t technical.

What will you cover after October?
We choose each month’s topic based on which AI breakthrough has just become most practical and valuable, so we don’t lock in the year in advance. We’ll announce each topic ahead of the session.

How is this different from the 10-week Agentic Academy cohort?
The cohort teaches the fundamentals of working with AI agents. The mastermind keeps you current after that, going deep on one new breakthrough each month.

I bought the bootcamp. Can I upgrade later?
Yes. After the October 2 bootcamp, you can upgrade to the full mastermind for $2,250. With the $500 you paid for the bootcamp, that’s $2,750 for the full year (compared with $2,500 if you join the mastermind now).

How do the one-on-one calls work?
The first 20 people who join the mastermind get six 30-minute calls: 2 with me, 2 with Wyndo, and 2 with Bonnie. Use them for help with your own builds, your strategy, or anything else.

Where does the community live?
In our WhatsApp group. Ask as many AI questions as you like, and you’ll get an answer from us within 48 hours.

Is there a refund policy?

Yes. If the mastermind isn’t right for you, email us at support@thoughtleader.school within 14 days of the first session and we’ll refund you in full. If you’re on the payment plan, we’ll refund what you’ve paid and cancel the remaining payments. The Sites bootcamp is fully refundable until it begins on October 2.

There are two ways for you to start.

If you want to stay at the practical frontier of AI all year, with a group of non-technical builders beside you, join the mastermind. Your first session is the October 2 Sites bootcamp.

Join the Mastermind

If you’d rather start with Sites and decide later, join the bootcamp on its own. If you upgrade after October 2, it’s $2,250 more to get the full year.

Join the Sites Bootcamp

Either way, we’ll see you on October 2.

Michael

P.S. The 6 one-on-one calls go to the first 20 people who join. Once those spots are filled, they’re gone.

Today, I want to go deep on one site I built so that you understand why I’m so excited.

A few details on it:

• It’s the first one I built.

• It took me 15 hours to create.

• A developer and designer would’ve charged tens of thousands for it.

• I use it 10+ times per day.

• It’s the one that showed me the potential of ChatGPT Sites.

It’s called See Then Build.

By understanding one example deeply, you’ll be able to better understand how Sites can make a big difference in your life and business.

With that said, let’s jump in.

Here’s the problem I was trying to solve…

Nobody Knows What AI Can Do, Including The People Who Built It

AI’s abilities aren’t installed by AI researchers. They emerge from training, which is why nobody can list them all, including the labs themselves.

With every release of an AI model, the gap between what AI can do and what people think it can do grows bigger. This gap is called Capability Overhang:

This increasingly large gap raises a question:

How do we stay on top of what AI can do right now as models explode in capability?

The best answer I’ve found isn’t reading model release notes or benchmark results. It isn’t even tinkering myself, though I do plenty of that. Rather, it’s this:

• Watch a curated feed of what millions of other AI tinkerers are building.

• Brainstorm how to remix what they’ve made with whatever I’m working on.

When you combine the millions of people experimenting with AI, you can tap into billions of collective hours of experimentation.

Someone else’s successful experiment does two things:

• It provides inspiration. It’s the equivalent of seeing someone run a 4-minute mile. Seeing is believing.

• It gives AI a reference. AI is really good at remixing something when it has a reference it can build on.

Each amazing AI result someone gets is a window into a capability that didn’t exist a few months ago.

But curating great AI demos is not easy with today’s tools:

• Looking for the posts on X is time-consuming.

• Going on to X is distracting.

• X’s bookmark system is notoriously hard to use.

• Posts are mixed in with hype, reposts, and noise. They disappear from my feed within hours. And when I want to find one a week later, it’s hard.

As a result of these challenges, I was spending 30+ minutes a day hunting for posts that I’d seen, but couldn’t remember where.

The most valuable signal in my feed was stuck inside the worst filing system I owned.

So I built a better one…

See Then Build Helps You Stay On The Frontier Of AI

I share the details of my system so that you understand how it works and can create your own version.

Every night at midnight, a ChatGPT scheduled task automatically searches X for new AI demos using the following rules:

• Certain keywords that match the ones I’m looking for

• A minimum number of likes

• A playable video or photo of the actual creation

• Details on the tools, prompts, and process that made it

I built an admin back-end section for See Then Build so I can easily add, edit, or remove searches:

The results go into a database. When I wake up, a new edition is waiting with lots of demos for me to check out.

This feed is the opposite of an algorithmic feed in many respects:

For each demo, See Then Build shows me:

• What it makes possible, in one line

• Three ways the idea could be used in real work

• An evidence label, so I know what’s been verified and what’s only claimed

• The creator’s original prompt or files, when they shared them

Then I bookmark the ones I want to come back to, tag them, add a note about what caught my eye, and search everything I’ve saved.

In September alone, See Then Build has surfaced 60+ super helpful demos.

That’s the core functionality, but this custom site also has a number of other features:

• An explore page that makes it easy to rapidly explore all the visuals in a collage format.

• A prompt builder that helps you recreate any demo you find on the site.

• A library of tools that the creators used to create the demos.

• A tech stack explainer so you can decide what tech is relevant for what you want to create.

• A library of tutorials related to creating things with AI.

• A Chrome extension to save an X post or website to the site.

And on top of all that, See Then Build also allows user accounts: after five visits, if you want to keep using the site, you’ll be required to enter your email address. Once you do, you can log in to start your own collection of saved inspiration and use the included tools to design your own projects.

Now that you understand the features, I’ll share five concrete examples of really cool things I’ve recently seen via See Then Build that inspired me to create things that I actually use.

Every Day, Someone Posts A Demo That Makes Me Stop Scrolling

Here are the five examples:

1. An Interactive, Narrated Bookshelf

2. A 3D App That Pulls Apart The Male Anatomy

3. A 3D Illustrated Explainer

4. A 3D Physical Space Walkthrough

5. A Cool Visual Light Show Effect

#1. An Interactive, Narrated Bookshelf

Credit: MengTo

This inspired a 3D book library we’re building into BlockbusterOS, which is an AI harness that helps people create blockbuster articles. You can see an early version of a bookshelf I created here in addition to a library you can move around.

#2. A 3D App That Pulls Apart The Male Anatomy

Credit: @ashebytes

This inspired me to create a similar tool for mental models.

#3. A 3D Illustrated Explainer

Credit: Haider

This inspired me to create an illustrated video explainer in the voice and tone of David Attenborough for my MODELS system. You can see a rough version of it below:

#4. A 3D Physical Space Walkthrough

Credit: Ethan Mollick

This 3D walkthrough inspired me to create a 3D interactive explainer for a MODELS concept that can’t really be shown in 2D.

#5. A Cool Visual Light Show Effect

This effect inspired me to create an animated logo with a similar effect for my Think With Models Site:

Bottom Line

Once you know something is possible and you have a reference, it’s very easy to remix it into your own version. That’s what See Then Build is designed to facilitate.

Now, I use it 10+ times a day.

Sometimes I browse for 30 seconds while something else loads. Sometimes I’m stuck on how to explain an idea, so I search my saved demos for a visual format I haven’t tried. In fact, I turned the site into a plugin, so I can simply tag it in Claude Code or ChatGPT Codex and have AI search for me.

But the bigger change is in how I think. When I see a demo now, my reaction isn’t “That’s cool.” It’s “How do I build that?”

But one of the most underappreciated features of Sites goes a level deeper. It’s possible to turn your site into a plugin and MCP just by asking for them. What this allows is magical…

You Can Turn Your Site Into A Tool That Makes Your ChatGPT Smarter

The See Then Build site is a bunch of data packaged in databases and displayed via a website. While a website is great because it’s easy to navigate, it’s not great for when you want to ask AI a question about what’s on the website.

By turning your Site into a plugin and MCP, you can query the data in your site. Like I do in the chat below:

What’s also really cool is that if I wanted to charge people a monthly fee for access to this data, I could easily integrate Stripe.

See Then Build Illustrates Three Things You Need To Know

I didn’t write this post just to show off a project I’m proud of (one I couldn’t have built a year ago without paying tens of thousands of dollars).

I share the Site as an example on three levels.

First, See Then Build is an example of many of Sites’ coolest features:

• User accounts

• Databases

• An admin area

• Design

• Interactions (browse, save, tag, and search)

• Custom URL

• Chrome extension

• Scheduled tasks/alerts

• Plugin/MCP

Second, See Then Build is a template for a type of Site that I plan to reuse over and over:

If you take a step back, you’ll notice that See Then Build is more than just a site for AI demos. Rather, it’s a research machine with four parts:

1. Search

2. Filter

3. Store

4. Page

The visual below shares the bigger picture of different types of research you could do with these four parts:

The same machine works for any topic where the best material is scattered and disappears fast:

• The best posts in your niche each day, so you never miss what your audience is talking about

• New research in your field

• Podcast episodes and talks featuring the people you want to learn from

• What customers are saying about your competitors

• Speaking opportunities, grants, or RFPs in your industry

• Every mention of your book, your brand, or your name

Said differently, See Then Build is the first version of a site template that I can use over and over again. Now that the template exists, the next one will take about a tenth of the time. I describe the new topic and the new rules, and then the machine gets rebuilt around them.

And again, I want to be clear about one thing: I’m not a coder. I didn’t write a line of code for this. I explained what I wanted, looked at what came back, and explained what to change, repeating this cycle until it worked.

Third, See Then Build is an example of a type of Site that I think is particularly exciting:

On the one hand, you can use Sites to create all of the typical sorts of Sites you might think of off the top of your head:

• A client portal

• A course website

• An article companion

• A personal brand website

• A company website

But on a deeper level, Sites is an opportunity to Sell Your Sawdust: an opportunity to turn things you already do while creating your end product into a standalone website you use yourself, and that others will pay for or trade their email to access.

In my next article in the series, I’ll explain how you can use ChatGPT Sites to Sell Your Sawdust too.

Over the last few years, I’ve spent a lot of time refining my X feed to follow the highest-signal people on the frontier of AI.

In May, I noticed something interesting.

Many were saying the same thing. They now preferred to get AI answers in HTML rather than plain text:

To name a few…

Andrej Karpathy: “This works really well btw, at the end of your query ask your LLM to ‘structure your response as HTML.’”

Lex Fridman: “For answers, I often have it generate dynamic html (with js)”

Thariq Shihipar, Anthropic: “I’ve started preferring HTML as an output format instead of Markdown.”

While I found the posts interesting, I didn’t quite get it.

I understood that getting some responses as an infographic or chart could be helpful. But a few things gave me pause:

• I wasn’t sure it was worth the wait to render the HTML.

• It felt like something I would only use rarely.

• The improvement felt incremental.

As a result, I didn’t do anything.

In June, I noticed something that strengthened the trend.

ChatGPT launched Sites on June 2. Sites made it easy for anyone to create a webpage, website, or app:

Still.

Like most people, I ignored it.

A website builder?

“Who cares?” I thought to myself. “There are already a ton of vibe coding tools. And Claude has Artifacts.”

Oh boy, was I wrong…

This Past Month Has Been My Most Exhilarating As An AI User

Over the last 4 weeks of nights and weekends, I’ve built 8 high-quality apps that help me with various parts of learning, thinking, and creating.

Three that I’m most excited about:

• Think With MODELS: By studying the biggest breakthrough ideas in history, I worked with AI to identify and feature 500+ cognitive moves people can use to think better. These moves are the primitives of mental models. Knowing them helps you think better and helps your AI think better.

• See Then Build: The latest versions of ChatGPT and Claude are true breakthroughs. They allow you to create things that were unimaginable a few months ago. The best way to explore what’s possible is to see what others have built and then remix it into your own work. But finding those examples takes a lot of manual work. That’s why I created this site. It surfaces the coolest things people are creating every day.

• Bestseller Playbook: This site features a navigable database of the most enduring New York Times bestsellers of all time, along with the patterns that made them successful. The goal is to identify the most powerful patterns for crafting and packaging ideas. The analyses aren’t done yet, but the site gives you a good flavor of what it will become.

The branding for each of these alone would cost $10,000. The programming would cost tens of thousands more for each.

With ChatGPT, I did it in dozens of hours for under $500 in AI credits without touching a line of code. Also, ChatGPT doesn’t charge anything for web hosting. Keep in mind that I’m not a designer or programmer.

To put the shift in context, there have only been a few game-changing moments that I’ve had with AI, and this is one of them:

In this post, I want to decode why ChatGPT Sites is amazing so you don’t sit on it like I did:

The Core Features Of ChatGPT Sites That I Love

What’s unique about ChatGPT Sites isn’t any one feature. It’s the combination of features that no other tool offers:

1. Document management

2. Website development

3. App development

4. All-In-One Platform

#1. Document management

Google Docs is still the best choice when you want to quickly create a document that others on your team can share and comment on.

But ChatGPT Sites excels when creating documents you want to share externally with clients, students, and the general public. For example:

• Sales proposals

• Paid article companions

• Teaching resources

• Client resources

A few features make Sites especially powerful for documents you share with clients and students:

• Beauty. As a non-designer, you can design beautiful documents that would normally cost a few thousand dollars. These docs wow people and build your brand.

• Usability. Rather than squeezing everything onto one page where people have to read top to bottom, you can create a user interface so people can jump between sections and pages easily.

• Extras. You can make pages with interactive elements, embedded video, databases, interactive 3D models, background animations, and much more.

Each site has sharing permissions like Google Docs. You can set any site, or any page in a site, to:

• Public. Anyone with the link can view it.

• Viewable by certain people that you add manually or that get added after purchasing.

• Editable by collaborators who are on the same business account. If you’re not on the same business account, you can collaborate using GitHub.

#2. Website development

You can build a full multi-page site, with a shared menu and a separate address for each page. Features include:

• Custom domain. You can easily add a custom domain to your site.

• Multimedia. Pages can combine text with playable media and compatible embedded content.

• Interactivity. You can make elements of the page interactive, which is great for teaching or explaining. This includes many types of forms and games.

• Forms & import/export. Collect answers through text fields, choices, dates, sliders, or a sequence of steps. Visitors can also bring a local file into a tool or take their work away as a file.

With website development, you can turn documents into websites:

• Member bonus → Paid member hub

• Client document → Client portal

• Teaching resource → Course platform

#3. App development

In addition to hosting static pages, you can host real apps:

• Login through ChatGPT. Visitors sign in with their ChatGPT account, so the site doesn’t need its own password system.

• User accounts. Once someone logs in, the app knows who they are, so it can save their work, keep their data private, and limit access to the right people.

• Interrelated databases. As in Notion, you can create many interlinked databases.

• Calculations & branching rules. The site can calculate an answer or choose a next step using rules you provide.

• Search, filters & sorting. Help someone find the right item inside a collection.

• Connections to other services through APIs. An API lets the site request information or an allowed action from another system.

• Turn a site into an MCP. This way, people’s AI can interact with the content of your website.

Granted, if you’re aiming to build a site for millions of users, Sites may not be the best fit. But if you’re aiming to quickly build a niche app, this could be a great fit.

#4. All-In-One Platform

One of the biggest benefits of Sites is that it is a single integrated platform:

• ChatGPT credits. You can use the credits from your ChatGPT monthly plan. You can accomplish an astounding amount of work with a $200/month plan.

• Browser use. ChatGPT has built-in browser use that is able to view websites and test them so you don’t have to leave your working environment.

• Computer use. ChatGPT can operate your computer and use any apps that the browser can’t reach. As of September, this feature works really well.

• ChatGPT memory. The AI already has all of your context and memory. This makes its responses much better.

• Alerts. You can easily set up an alert in ChatGPT that activates hourly, daily, or weekly to execute a prompt and then update a website. Rather than getting update alerts in your chat, you can see them all on the regenerated website, which allows you to sort, search, and filter them.

This all-in-one integration enables a seamless, affordable, easy-to-use, fast experience that allows you to create beautiful apps even if you’re not a designer or coder.

You literally explain what you want in plain English and then see it built. Then, you explain what you want changed and see it improved.

These Features Give You Superpowers

• Create a landing page or landing site. I created a website, Think With MODELS, where viewers can only visit 5 pages on the website before they’re required to give their email address.

• Create a new subscription hub. I’ve now created 8 websites. Using a Stripe integration, I can make it so that once someone pays, they get access to members-only content. This may give a site like Substack a run for its money.

• Create a client resource. If you’re a coach, you probably have all of the related documents from your calls in Google Docs. With Sites, you could create a hub that has transcripts, to-do items, and a customized AI bot that your clients could talk to.

• Create a course platform. If you’re hosting a course, it may actually just be easier to make it a site. This could offer a cheaper, custom alternative to sites like Kajabi and Teachable.

• And much more…

Why Sites Is Particularly Powerful Right Now

Sites became even more powerful in September for three reasons:

• OpenAI updated its core ChatGPT model on September 3. Astra, the newest model, is significantly smarter. It is especially good at computer use, browser use, and creating 3D models.

• OpenAI upgraded its image model on September 8. This model makes images and user interfaces significantly better.

• TypeSafe AI released a new class of AI model called Jev on September 15. On the quick decisions it’s built for, Jev is 40 to 200 times faster than today’s leading models and, at its best, more than 400 times cheaper. This is a big deal, because it’s now possible to cheaply integrate AI into any site and make the site smart.

Learn How To Get Started With Sites This Week

I couldn’t fit everything about Sites that I wanted to in one post, so I’ve decided to create a series.

In the coming week, I’m going to share the coolest things I’m creating with Sites, along with the best examples I’ve found from others. And I’ll share how-to tips and prompts with paid users.

To my knowledge, it will be the most comprehensive explainer of Sites on the Internet.

Stay tuned.

This month’s Blockbuster Live is my attempt to make sense of that shift while it’s still happening — not in hindsight, but live and in real time. I walk through what Self-Organizing AI actually looks like inside Claude Code, why “evals” are quietly becoming the highest-leverage skill in this new paradigm, and how I’m rethinking thought leadership itself now that AI can stress-test an idea the way we used to stress-test code. I also go deep on a concept most people have never encountered — cognitive artifacts — and why it may chan…

Read more

They’re rebuilding an entire software app from scratch, in a different programming language. To check their own work, the agents are running this loop:

Nobody is supervising these agents. At some point, the agents even opened a chat channel in Slack and started posting screenshots of their progress every few minutes, the way an eager new hire might, so anyone curious could follow along.

You might assume that this level of autonomy required a team of engineers. An elaborate setup. Some secret configuration. But the entire instruction that set all those agents in motion was just one paragraph:

“I want you to rewrite the Electron app in Swift. I want you to run the Electron app in the Mac virtual machine, screenshot it, and then look pixel by pixel, compare it to the Swift version. Don’t stop until you’re done.”

In plain terms:

• Goal: Rebuild an app from scratch in a different language.

• Check: Keep checking your own work against the original.

• Permission: Until it’s flawless.

That’s the whole thing.

Cherny explains the whole story in this short clip that is a must-watch:

This one sentence from him captures why it’s so exciting:

“Everyone here could do it.”

He’s right. You could write that too. But you probably haven’t.

Instead, most AI users are:

• Babysitting their AI step-by-step.

• Creating elaborate multi-phase prompts.

• Simply using AI to answer questions.

There’s nothing wrong with any of that. But it’s a fraction of what AI can already do for you, and most never find out.

No One Knows The Actual Limits Of AI

When you ask AI for something small, it hands back something small and everything feels fine.

• The email gets written.

• The transcript gets tidied.

• The slides get formatted.

You got what you asked for. There’s no pop-up box warning you that you just used a supercomputer like a calculator.

With other software, all of the features are hand-coded into the app. You access them via menus and buttons that have been designed for you. There are manuals that cover everything.

But AI is different.

With AI there is no cheat sheet, because AI’s abilities weren’t installed. Rather, they emerged from training. This means nobody knows what AI is truly capable of, including the people who built it. It also means that you can uncover AI capabilities that no one else in the world has.

That silent gap between what AI can do and what users ask of it is the whole problem.

Cherny has a name for this gap: overhang.

Source: Interview At Y Combinator

“The model is able to do all sorts of things with today’s models, not a future model, but today’s model, that we have not yet realized. There’s this overhang because the model can do this at every given model generation, but there is often not a product that lets the model do this.”
—Boris Cherny

The capability is already sitting there, unused. What’s missing is a person who thinks to hand over the bigger job that exposes AI’s latent abilities, and a setup that lets the model see it through.

Bottom line:

Cherny’s two-week app rebuild from one paragraph was not a fluke. It’s an emerging prompting pattern among the most advanced AI users that taps into AI’s latent abilities. And what’s amazing about the pattern is that it’s both very easy to learn and easy to use. I call it ultraprompting.

An ultraprompt is a single prompt with three parts: a goal, a way to check the work, and permission to keep going.

The goal names the finish line. The check is a scoreboard the AI can read without asking you. The permission — “don’t stop until you’re done” — is what turns a single answer into two weeks of work.

Remove any one of the three and the pattern collapses. No goal, and the AI has nowhere to go. No check, and it can’t tell better from worse. No permission, and it hands you a draft and waits.

An ordinary prompt asks for an answer. An ultraprompt hands over a job.

This article will teach you to write one.

The World’s Top AI Creators Are Getting Astounding Results With Single Prompts

Here are a few ultraprompt examples that have blown my mind:

1. An Anthropic engineer rewrote core software with one prompt.

2. A prompt that loops autonomously maintains important code.

3. A simple prompt beat an “impossible” benchmark.

4. One prompt recreated a famous game.

5. Google built an operating system with one prompt.

6. A one-page prompt conducted 700 autonomous experiments in two days and got astounding results.

#1. An Anthropic Engineer Rewrote Core Software With One Prompt

Bun is a foundational piece of software that other programs are built on top of, including Claude Code itself. Bun’s entire codebase, over 500,000 lines, needed to be translated from one programming language to another. That’s the kind of rewrite that, in Cherny’s words, would have taken human engineers “definitely over a year.” Instead, it was handed to Claude Code as one job. “It was one prompt,” Cherny says. It finished in 11 days, with a single engineer checking in on the AI’s progress rather than a team doing the work.

#2. A Prompt That Loops Autonomously Maintains Important Code

Claude Code has started to maintain its own code. Every day, standing agents go out to hunt for bugs and fix them, work that Cherny describes as “doing the work of dozens or hundreds of engineers.” Nobody is typing requests for fixes each morning. The agents were set up once, and they run repeatedly.

In Cherny’s own words:

“We have a Slack channel where we just had Claude start a bunch of different routines to maintain its own code base. For example, one routine is clean up dead code. This is a single prompt. It’s like one sentence. Claude runs this every day. It’ll look for dead code across all the code bases using static and dynamic analysis. We didn’t prompt that. It just kinda figured it out, and it’ll put up a request every day to delete the dead code.”

#3. A Simple Prompt Beat An “Impossible” Benchmark

AI researcher Jeremy Berman recently posted the best score anyone has recorded on ARC-AGI-3, an AI IQ test made of 25 puzzle games the AI has never seen and must figure out with zero instructions.

The same model playing by itself wins about 30% of the time. Berman’s version won 96% of the time.

You’d assume he built an elaborate puzzle-solving machine to get there. But he didn’t.

Here is his own description of the entire system:

“…basically Claude Code + Opus 5 (high), one action command, and filesystem logs. Almost nothing ARC specific.”

Translation: The “one action command” is a tiny program that works like a game controller wired to a tape recorder: its only job is to press a button in the game, then write down what the screen looked like afterward. And the “filesystem logs” are that written record, an ordinary text file that became the AI’s memory.

Rather than trying to hold 25 games’ worth of detail in its head, the AI did what a careful scientist does: it wrote every move and result in its notebook, went back and reread its own notes, and kept a running list of what it had figured out, with each finding labeled by how sure it was, from “confirmed so far” down to “untested.”

#4. One Prompt Recreated A Famous Game

This one you can see with your own eyes. An AI founder named Matt Shumer typed a single prompt into Claude Code with “ultracode” turned on and walked away. (“Ultracode” is Claude Code’s highest effort setting, which allows it to take on bigger, self-directed jobs.)

Here’s what Shumer typed in word-for-word:

“I want you to build a first-person shooter at the level of the most recent Call of Duty games. It should be utterly perfect, visually beautiful, with every single thing done at AAA quality—from textures to physics to anything you could think of.

Fan out sub-agents and have sub-agents tackle each one individually so that the game is utterly perfect. You should /loop on each item and have a separate sub-agent check it visually to ensure it looks triple A. That separate sub-agent should be a really harsh critic, and if it doesn’t look triple A, it should keep going.

Don’t stop until each sub-agent is utterly wowed with the quality when compared with the actual Call of Duty game. It should literally compare them side by side blind and say which one looks better. Do this in ThreeJS. /loop until it’s utterly perfect. Fan out sub-agents and ultracode.”

What came back, hours later, was a playable 3D shooter in the style of Call of Duty: roughly 55,000 lines of code, with every texture, animation, and sound effect generated by the AI itself. Nobody touched it mid-run. The demo racked up millions of views:

Then people started creating their own versions of the prompt. One builder pointed it at the racing game Burnout Paradise and got back a drivable, drift-and-crash game he put online for anyone to play.

Here’s the part that matters for you: Shumer’s prompt contained no game-making expertise. What it contained was a management structure. He calls it the Gauntlet Loop:

1. Give a lead AI the goal, plus a concrete example of what great looks like. For the game, that example was actual screenshots of Call of Duty.

2. The lead AI breaks the job into small pieces, and every piece gets two AIs: a builder, and a separate critic whose only job is to hold the work up against those screenshots and name the biggest gap it can find. (The builder and the critic are never the same AI, for the same reason a writer shouldn’t edit their own draft: the one who made the thing always believes in it a little too much.)

3. If the real screenshot still wins, the builder goes back to work. The loop doesn’t stop until the builder’s version wins.

#5. Google Built An Operating System With One Prompt

In May 2026, Google’s team published an experiment. They gave their own coding agent, called Antigravity, a single prompt asking it to build a working operating system. (An operating system is the foundational software that runs a computer, like Windows or macOS.) It’s the kind of project computer science legends spend years on.

The main agent spun up 93 sub-agents, made 15,314 separate requests to the AI model, and delivered a very basic system that boots and runs the classic video game Doom. Nobody guided it or corrected it along the way. The total cost at standard prices was just $916.92.

#6. A One-Page Prompt Conducted 700 Autonomous Experiments in Two Days And Got Astounding Results

The last example comes from Andrej Karpathy, and it hints at where all of this is heading. Karpathy is one of the most famous AI scientists in the world. He helped found OpenAI, the company behind ChatGPT, and he ran the AI team at Tesla.

In March 2026, he released a project called autoresearch that hands AI the one job everybody assumed would stay human the longest: making discoveries about how to improve AI.

Training an AI model normally takes enormous computers running for months, but Karpathy built a tiny practice version, one that can be trained from start to finish in five minutes.

Then he wrote a one-page instruction sheet and let an AI agent loose on it. All night long, the agent plays scientist.

1. It comes up with an idea that might make the training work better.

2. It runs a five-minute test.

3. It looks at the score.

4. If the idea helped, the agent keeps it.

5. If it didn’t, the agent throws it away and tries something else.

No one is watching. No one needs to be, because the score settles every argument. In one two-day run, the system worked through roughly 700 experiments and ended up with 20 improvements that survived testing — enough to cut training time by 11%. Each one is a small, genuine discovery about how to train AI better, and no human thought of any of them. One of them wasn't even an improvement so much as a correction: the agent noticed that Karpathy had never applied regularization to part of his own model. One of the best AI researchers alive had simply never caught it.

One of the discoveries wasn’t even an improvement so much as a correction: the agent found a genuine bug in Karpathy’s own code, a mistake that one of the best AI researchers alive hadn’t noticed.

What’s Going On Here?

In the last few months, we have crossed a critical ability threshold that is allowing agents to create incredible results from single prompts that run for hours or days.

Following the trends, we can expect this phenomenon only to grow. An AI research organization named METR gauges AI’s progress with a human yardstick: how long a task would take a skilled person to do. By that measure, the tasks AI can finish at 50% reliability doubled in length roughly every seven months from 2019 to early 2025, from tasks that would take a person a few seconds to tasks that would take a person about an hour.

Since then the pace has accelerated. From 2024 onward, the doubling time has fallen to under three months, and by mid-2026, the best models were finishing tasks that would take a person around twelve hours (and METR says its test is almost out of tasks hard enough to challenge the newest models). Fast forward a few years and AI will be productive for months at a time.

Here’s another way of looking at the implications:

• On one hand, AI is getting smarter and longer-working (growth).

• On the other hand, what most people ask of AI has barely moved since 2023 (stagnation).

Overhang is the space that grows between them as one compounds while the other sits still.

Therefore, the cost of asking small roughly doubles every seven months. In 2023, asking small left maybe a paragraph on the table. Today it leaves two weeks of unsupervised work you never knew you could have had.

Maybe you don’t write code, but the same gap exists inside your work too. The rest of this piece is about how this applies to you and how you can close your own gap…

The 3 “Magic Words” That Tap Into AI’s Latent Potential

There are three names for the machinery an Ultraprompt sets in motion, and they can all be triggered inside Claude Code when you use the “ultracode” effort level setting:

1. Dynamic workflow

2. Goal

3. Loop

Amazingly, each of these features was launched in the last few months, which means that their potential is still largely unexplored.

#1. Dynamic Workflows

What Cherny calls a dynamic workflow is Claude Code taking one job that’s too big for a single agent, breaking it into stages, and spinning up as many copies of itself (sub-agents) as the work needs. A single dynamic workflow can generate dozens, hundreds, or even thousands of sub-agents.

Let me be more concrete. Picture a project manager who hires several temporary teams on the spot:

1. Team #1 (divergent thinking): Brainstorms different ways the project could be done.

2. Team #2 (convergent thinking): Shortlists the top ideas.

3. Team #3 (red teaming): Stress-tests the shortlist to pick one path.

4. Team #4 (implementation): Splits the work and builds.

5. Team #5 (verification): Confirms the work was done to specification.

The 11-day Bun rewrite ran exactly this way. Starting a dynamic workflow is absurdly easy. As Cherny puts it:

“All you have to say is, ‘Use a workflow.’ That’s it.”

When to reach for it: any time the real job is too big for one pass and splits naturally into stages. Say you have a folder of 60 customer-call transcripts, and you need every one read, the complaints pulled out, grouped into themes, and written up. That isn’t one task. It’s a first pass across all 60, a second pass to cluster what they share, and a third pass to write the summary. A dynamic workflow runs all three passes, using as many sub-agents as needed to make sure each part of the job is done at high quality.

How to start one: you don’t set anything up. Describe the whole job in plain English and add three words at the end: Use a workflow.

For example:

Use a workflow to turn the customer call files in this folder into a findings report. Stage 1: fan out one agent per .txt file, reading them in parallel, and have each agent pull the single biggest complaint plus a short quote from its file. Stage 2: a second agent groups those complaints into themes and counts how many files fall under each theme. Stage 3: a final agent writes a one-page findings summary with the themes ranked from most to least common.

You’ll see Claude start the fan-out:

#2. Goals

What Cherny was doing when he set that two-week rebuild loose was stipulating a destination and a stopping condition. A goal is exactly that: you name the finish line and let Claude keep working until it’s genuinely reached.

It’s like telling a contractor, “repaint this room, and I’ll know it’s done when there are no streaks in daylight,” then letting them work without you standing over the brush.

How to start one: In Claude Code you can state a goal in plain words, the way Cherny did:

• Destination: “I want you to rewrite the Electron app in Swift.”

• Stopping Condition: “Don’t stop until you’re done.”

Alternatively, you can type /goal in Claude Code followed by the finish line to make Claude check its own work against it every round.

When to reach for it: any time you’d want to say “keep going until it’s actually good, and check your own work.”

For example, let’s say you want your muddled About paragraph rewritten until a stranger gets what you do in one read. You type the finish line as a goal, and Claude drafts, checks itself against that finish line, and drafts again, instead of handing you one version and waiting.

Your ultraprompt might be:

/goal the paragraph in about.txt is clear enough that a stranger could say what I do in one sentence, or stop after 5 rounds

#3. Loops / Routines

A loop is the pattern behind Claude’s daily code-maintenance agents (as described by Cherny): it’s Claude Code taking the same action over and over until a desired result is achieved. You set it up once, and it keeps going.

Loops are the opposite of a workflow. Instead of one giant job split into stages, a loop is one small job that repeats until finished.

Cherny’s description of the maintenance jobs is the whole idea in two lines:

“This is a single prompt. Claude runs this every day.”

When to reach for it: any time you want Claude to keep an eye on something while you work. Start a slow build and have it check every few minutes, then tell you the moment it finishes. Or point it at a folder and have it summarize each new file as it lands, while you keep writing.

How to start one: type /loop, how often you want it, then the task.

For example:

/loop 10m check this folder for new files and summarize anything that's been added

You’ll see:

What’s the difference between a loop and a routine?

• A loop runs inside an open Claude Code session on your computer at a set interval, and if you close the session or turn off your computer, the loop stops.

• A routine is a prompt that runs at a scheduled time or in response to a specific event. Routines run in Anthropic’s servers, rather than on your computer, allowing Claude Code to perform tasks even while your computer is off.

What This Looks Like When You Aren’t Technical

All of the examples above came from engineers.

Leandra is not one. She is a student in The Agentic Academy, which I co-teach with and .

But last weekend, she built her own Mac writing app from scratch after we taught her about dynamic workflows in class. She wanted something like OmmWriter, the distraction-free writing tool, but personalized for her unique needs.

During class, I asked folks for their learning victories. Here is what Leandra shared:

“It’s a simple app [that] was entirely a product of curiosity. Instead of downloading an app not optimized for my needs, I simply went to Claude Code.”

She describes the app that Claude built for her:

“It’s optimized for not having any distractions, so it’s a full window. Pretty backgrounds, these peaceful sounds, and then there’s this clicking, this typewriter sound when you type.”

Leandra didn’t design any of that in code. She described it. Here was her whole prompt:

I essentially want to build a replica of the OmmWriter app, but save it in Markdown instead of RTF. I would prefer it to be an app that lives on my computer and opens saves locally.

“That’s all I wrote,” she said. “And then Claude Code asked me some questions about what I wanted, and we went back and forth.”

Claude offered to build her app in Swift, so it would use less memory and run faster. Leandra agreed, and came back later to a finished program that had even named itself. “It named it Hush, and a few minutes later, it installed it. I’m so happy.”

Notice what she didn’t do. She didn’t lay out the steps, or the file structure, or research how you package a Mac app. She just handed over the whole outcome and answered a few questions along the way.

“It was not a crazy prompt,” she said. “We think we need to know so many skills. We really just need to know the command language. Even if you don’t know how to do it, you can accomplish it now.”

Think about doing the same thing that Leandra did, for all of the simple apps you spend hundreds of dollars on per month.

The “Magic Words” Combine Into A System

At first glance, these stories appear to prove that a few magical words can unlock enormous amounts of work.

That is the wrong lesson.

The initial ultraprompts did not contain all the intelligence required to translate Bun into a new programming language, maintain Claude Code, solve unfamiliar games, recreate Call of Duty, build an operating system, recreate a writing app for focus, or conduct 700 experiments.

Instead, the “magic words” created the conditions for the AI to organize its own intelligence.

Each of those examples reveals a different part of this emerging management system:

1. Boris Cherny’s software rewrites show the power of delegating an entire outcome rather than feeding AI one small task at a time. Claude worked out the steps on its own and kept going for days.

2. Anthropic’s recurring loop agents that maintain its software show that a prompt can create an ongoing responsibility, not merely request a one-time answer. The AI finds the work, performs it, checks it, and returns again the next day.

3. Jeremy Berman’s ARC-winning system shows the importance of memory, experimentation, and tool creation. Claude recorded what happened, formed hypotheses, tested them, and built whatever parsers, simulations, and search programs the problem demanded.

4. Matt Shumer’s Gauntlet Loop shows the value of an AI organization. A lead agent divides the work among builders while separate critics compare each result with a concrete example of excellence and send weak work back for revision.

5. Google’s operating-system experiment shows that one agent can coordinate an enormous tree of sub-agents, distribute thousands of pieces of work, and assemble them into one functioning result.

6. Andrej Karpathy’s autoresearch project shows what happens when experimentation becomes fast and the score settles every argument. The AI can generate a hypothesis, run an experiment, measure the result, preserve genuine improvements, and repeat hundreds of times without waiting for a human.

7. Leandra’s writing app shows that none of this belongs to engineers. She described the outcome she wanted in plain English and answered a few clarifying questions, while Claude made the technical calls she couldn’t: choosing the programming language, designing the app, naming it, and installing it.

These systems do not all use the same method. Some problems need many competing ideas. Others need parallel workers. Some need a builder and a critic. Others need a standing agent that returns every day. When the route is obvious, the AI may simply need to execute it.

The shared pattern is deeper:

In the old prompt-engineering model, the human tried to specify the steps:

“Here is exactly how I want you to think and what I want you to do. First do [x]. Then [y]. Then [z].”

In the emerging model, the human designs a small, self-correcting organization:

“Here is the outcome, the evidence that will prove you succeeded, the environment you can operate in, the boundaries you must respect, and the budget you can spend. Decide how to organize the work, test the results, and improve them.”

The prompt becomes less like a recipe and more like a combination of five things:

• a job description (the outcome you hand over).

• an org chart (the team of agents it organizes).

• a laboratory (where it tests ideas).

• a quality-control check (that verifies the work).

• a notebook (the memory it keeps).

The most important ingredient is not autonomy by itself. An AI can autonomously produce bad work forever. The leverage comes from autonomy connected to trustworthy feedback.

Karpathy’s AI had a numerical training score. Berman’s agent could see whether its moves won the game. Shumer’s critics had Call of Duty screenshots. Claude’s coding agents had tests, static analysis, and working software.

The clearer the signal separating better from worse, the more safely the human can step out of the loop.

There is also another important limit. “Keep going until it is perfect” may work as a dramatic demo, but it is not a responsible general rule. An autonomous loop also needs a budget, boundaries, escalation conditions, and a finish line: a clear definition of when success is achieved or further work might no longer worth its cost.

Bottom line:

An Ultraprompt is the seed of a temporary organization that can build, test, criticize, remember, and improve its own work.

The implications of this way of talking to AI are profound and require a new mindset. Put differently, as AI evolves, we need to evolve how we interact with it.

PAID SUBSCRIBERS:
Read the full article, get a tutorial, and download the ultraprompting skill

You’ve now seen what one paragraph of plain English can set in motion. You know the magic words, and you know the truth underneath them: the words aren't the secret. The conditions they create are.

In the rest of this article, you learn to create those conditions yourself, starting with your own work. You’ll learn:

• The two mindset shifts that separate people getting these results from everyone else. The prompting instincts you learned on older models are now the thing holding you back. See what replaces them.

• The complete 7-step protocol for handcrafting an ultraprompt, with ready-to-use language you can copy for every step. Hand over whole jobs, set destinations instead of routes, and let the AI find its way there.

• The future we’re heading toward: the second-order implications most people won’t see coming until it’s too late.

In the next two weeks, you’ll also get:

• Claude Code tutorial so you can install the desktop app, if you haven’t already. Unfortunately, Claude Code is very challenging to install for non-technical people without guidance. Once you install it, you’ll be able to ultraprompt and use the magic words. Our team has helped dozens of people install the software, so we know where people get stuck.

• Ultraprompting skill that runs this method for you. We’ve spent a few weeks creating an AI skill that identifies ultraprompting opportunities for you and then creates one for you.

• Personal case study. I’ll share my most successful ultraprompting experiments as step-by-step case studies as well.

Read more

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.

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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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PAID MEMBER BONUS:
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

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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.

Take Action

• Join Cozora to get more demos like Wyndo’s from other amazing creators. Paid subscribers of this newsletter get a 50% discount.

• Join the inaugural cohort of the Agentic Academy of Knowledge Work.

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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):

• Learning Ritual (8 weeks): Learn how to 5x your learning speed and quality while remembering more. Refined over 2 cohorts. ($1,000 value)

• Seminal (year-long): Learn how to create blockbuster articles. Refined over 5 cohorts, with 25+ hours spent creating and improving each class. ($5,000 value)

• Mental Model Club (50 manuals): Learn the 50 most useful and universal mental models. Each model includes a manual and an on-demand class. It’s the output of thousands of hours of research and application ($500 value)

• The J-AI Newsletter. J-AI shares the top frameworks and accompanying AI prompts of legendary business strategist icon Jay Abraham. Over his 50+ year career, Jay has helped businesses in 1,000+ industries generate $75B+ in documented additional revenue. We manually add you to the J-AI newsletter within 48 hours of joining. ($600/year value)

• 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:

1. Visit the Blockbuster Blueprint subscribe page

2. Select the Founding option when purchasing

3. Get the discount code from the perks page

4. Use the discount code on the Agentic Academy checkout page

Step #1: Visit the Blockbuster Blueprint subscribe page

Become a Founding Member

Step #2: Select the Founding option when purchasing

Step #3: Get the discount code from the perks page

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.

Visit The Perks Page

Step #4: Use the discount code on the Agentic Academy checkout page

Get Cohort + Ongoing Access

Get 10-Week Cohort

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:

Subscribe now

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