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 14, 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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Steps to AI Blockbusters

Most people using AI for writing are leaving enormous value on the table. They prompt once, accept the output, and move on. But the writers and thinkers who are pulling away from the pack aren’t doing that — they’re building systems: systems that encode their mental models, break down the creative process into deliberate steps, and use AI to explore a whole possibility space, not just confirm what they already think.

That’s what this session of Blockbuster Live is about. “Steps to AI Blockbusters” is one of the core sessions in my thought leadership course, where I teach serious writers, experts, and entrepreneurs how to produce ideas that travel — and how to build the AI-powered workflows that make it possible to do that consistently, at scale. If you’ve been wondering what separates AI-assisted content that’s forgettable from content that actually moves people, this is the session to watch.

Read more

In 2025, context engineering blew up as a skill:

All at once, everyone realized that their AI gave dramatically better answers when provided with enough context.

In 2026, mental models is the opportunity.

With mental models, you can functionally upgrade your AI from Claude Opus 4.7 to Claude Mythos.

To understand how and why, you first need to understand three things:

1. Why more intelligent AI can be worth $30,000/month

2. How everyone can make their model signifantly smarter

#1. How More Intelligent AI Can Be Worth $30,000/Month

A few weeks ago, I was speaking at a mastermind of entrepreneurs with 7-8 figure businesses. I asked everyone how much they would pay per month for early access to Claude Mythos, Anthropic’s big new model that’s so powerful they couldn’t release it to the public.

I started the bidding at a few hundred dollars per month, and it went all the way up to $20,000 per month.

That’s how valuable intelligence is.

Here’s another way to think about it. I’ve been happily paying $200/month to access the latest models of Claude and be a heavy user. Theoretically, I could just use open source, local models, and pay less. They’re just six months behind. But I’d rather pay extra to be on the frontier.

Now that you understand how important intelligence is, it’s critical to realize that you, as an individual, can actually increase your AI's intelligence even if you’re not at a big lab….

#2. How Everyone Can Make Their Model Significantly Smarter

At first glance, it would seem that there is no way to make the AI we get from labs smarter. They have huge teams of the world’s smartest people with the biggest budgets all focus on making AI smart.

This is obviously not the case. Here are a few examples of research that shows that fairly simply interventions can have a big impact:

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

• Asking the same question several times and keeping the most common answer raised grade-school math accuracy from 56.5% to 74.4%, a jump of almost 18 points.

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

• Telling it to name the governing principle before solving the specific problem raised accuracy by 7 to 27 points across physics, chemistry, and multi-step reasoning.

• Letting the model select and combine its own reasoning moves for a task beat plain step-by-step reasoning by up to 32 points, and beat the sample-and-vote method above while using 10 to 40 times less computation.

• Giving it a library of reusable thinking templates to pull from improved results by 11% on one puzzle and 51% on another over the previous best.

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

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

• Combining several weaker models so their blind spots cancel let a committee of open-source models score 65.1% on a standard benchmark, beating GPT-4o at 57.5%.

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

More recently, I created a mental model system prompt that makes your AI smarter, and I wrote about it here:

Whenever I talk to readers one-on-one, they point out how much better the mental model-enhanced response is compared to the generic AI response.

To understand why, you need to understand one of the simplest and most powerful mental models that almost no one knows about…

The Universal Intelligence Framework Explains The Power Of Mental Models

There is a simple 4-step formula that EVERY intelligent system in the universe has, whether it’s a human, a rabbit, or AI:

Here’s how it works for humans in a nutshell:

• We take in information. We humans take in information from direct experience, from consuming knowledge, and from other people.

• We process that information. We use a combination of conscious and unconscious algorithms to make sense of that data, connect it to other knowledge we have, and update our existing understanding of the world. Two people looking at the same data could process it in completely different ways.

• We experiment. After making sense of new information, we take action in the world. For example, after we read a book, we might get a few ideas of things we’d like to try. So we turn ideas into action items, experiments, and projects.

• We get feedback. As we take action, we receive feedback from our environment or our dashboards. Then, use that feedback to update how we go through the next loop.

Organisms that learn faster can perform each step more quickly and effectively.

This model is so powerful and simple that it served as the bedrock of an Accelerated Learning course I taught for years.

It’s particularly powerful in this moment, because it gives us four ways to make AI smarter…

The #1 Insight The Model Gave Me On How To Make AI Smarter

A. Information

Using this model, I was able to contextualize context engineering. Providing the right information to AI is just the first of four steps in the model.

B. Algorithms

Once AI takes in information, it has to make sense of it:

• Categorize it

• Prioritize it

• Contextualize it

• Sequence it

• Connect it to other knowledge

• Synthesize it

The algorithm stage is how the information gets processed. Two people (or two AIs) handed the same data can reach completely different conclusions depending on the algorithms running underneath.

Mental models are the algorithms. When you tell AI to think through second- and third-order effects, or to reason from first principles, or to apply the Pareto principle, you’re handing it a processing operation it likely wouldn’t have used by default.

C. Experimentation

After processing information, an intelligent system has to act on it and then see what happens. I call this experimentation rather than “action” because the point isn’t just to do something, it’s to test whether the ideas work and learn from the result.

Here’s what’s changing: we used to be limited to one shot. Write one article, ship it, wait for feedback. Now the rate of experimentation can jump by orders of magnitude. Instead of one version, you can have AI generate ten different approaches, take the learnings from each, choose the best, or synthesize a new best. One system I’m building inside Claude Code tests an article’s title and intro against real paid ads—running 30 titles past a lookalike audience to see which hook actually earns clicks before I commit to writing. The constraint was never the number of ideas; it was the cost of testing them. AI collapses that cost.

D. Feedback

The final step closes the loop: you take in what came back and use it to run the next cycle better. This is where evals matter most. The better you can define your goal, how to measure it, and a rubric AI can use to judge “did this work or not,” the more your system can iterate on its own until it reaches something good.

This is also the step where the deepest learning lives. Most people, when AI gives them a flawed output, just say “fix it” and move on—they change the single thing in front of them. But you can use that feedback to make bigger changes: not just what was wrong, but what thinking led to it, and how to update that thinking so the same error never recurs. That’s the difference between correcting an output and improving the system that produces outputs. Andrej Karpathy’s recently released Auto-research points at where this is heading—feedback loops tightening to the point where the system improves itself.

Session Overview

In this session, I:

• Go deeper on why mental models are the hidden multiplier inside every high-performing AI system.

• Give an overview of mental models

• Share Charlie Munger 5-step approach to using mental models

• Explain the challenges of using mental models without AI—you have to remember them, pick the right ones, and run them in the right sequence in the moment.

• Demonstrate how AI overcomes each challenge

This class is about the architecture underneath AI — and once you see it, you can’t unsee it. The gap between people using AI casually and people who have built mental-model-powered systems is widening fast. This session is about crossing that gap.

I built this class as part of my ongoing Blockbuster Live series, where each session covers one high-leverage idea at the frontier of AI and learning, and gives you something concrete you can apply immediately.

During this session, we:

• Explored why mental models are the hidden multiplier in AI productivity

• Traced the full prompt progression, from chat to skills to autonomous systems

• Walked through the three core ways to embed mental models into AI workflows

• Saw how mental models function as a “command language” for better thinking

• Examined Gary Tan’s GStack system, built on dozens of embedded mental models

• Live-demoed a system prompt that automatically applies mental models to every query

• Shared the G-Brain mental model encyclopedia and how to integrate it into Claude

• Discussed the learning loop — how AI improves not just output, but your own thinking

• Heard a live case study from a participant using fully autonomous AI systems

• Answered participant questions on debugging AI errors, system design, and skill-building
  

AI-Generated Podcast Summary Of The Class

How To Access The Full Course

Free members get a 30-minute video preview of the class.

Basic paid members get:

• Access to a monthly 90-minute class for 12 months.

• Prompt to create specialized profiles for any context

• Class resources (chat transcript, slides, full class transcript, prompts that are shared)

Said differently, paid members get access to 18 hours of learning for just $20/month or $149/year. This comes to just $8 for every hour of live class. And this doesn’t even include our other live monthly class, Augmented Awakening, or over $2,500 in other perks (20+ prompts, 7 courses, 3 books, blockbuster article library, etc).

Subscribe now

RECORDING RESOURCES

1. Presentation Slides

2. Blockbuster Live Prompts

3. Class Transcript

4. Other Classes In The Blockbuster Live Course

5. Resources Shared

6. AI Timestamps

7. AI Chapter Summaries

8. Chat Transcript

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This isn’t my usual article.

A few times a year, I come across an idea so big it changes my life. When that happens, I drop everything and go all out to make sure it lands with you the way it landed with me.

This is one of those.

What follows connects dots most people are missing. And, it takes you behind the scenes of a small group of knowledge workers at the frontier who are already 20x’ing their productivity.

This article went through 20+ drafts and is the culmination of 100+ hours of research. It’s years of tracking and thinking about AI condensed into one read.

Article

“Why are these people making the world so hard for me to live in? Everything worked fine before.”

My mom said this to me last year, and it sent a dagger into my heart. She is 74, retired, and was, I want to emphasize this, a computer engineer.

My mom is still the most independent-hearted person I’ve ever met, and I immediately knew what she was really saying. The shift was causing her to lose what she valued most: her independence.

The same pattern kept repeating:

• Her old device would break (phone, TV)

• She would get the new version, which was “smart” by default

• The setup and usage would be overwhelming

• She’d spend hours trying to figure out something that would take someone else minutes.

• Until eventually, she’d either give up or get help.

With each new device that went “smart” and each offline process that went online, her independence eroded.

She did not see this coming. Almost nobody who gets left behind ever does. But the world was becoming more and more alien to her, and it felt like there was nothing she could do about it.

I’m writing this article because the same thing is about to happen again, on a drastically faster timeline, to a much larger group of people. And I think there’s a real chance that you’re one of them.

In fact, a huge percentage of people are already wondering the same thing about AI that my mom wondered about technology:

Why is this small group of people in Silicon Valley creating something that will completely disrupt my life, my plans for the future, my local community (in the case of data centers), and my decades of expertise that I’ve gone into debt for?

The resistance isn’t coming from where you’d expect either. This time it’s not just retirees struggling with new interfaces. Graduating seniors are booing commencement speakers for telling them to embrace AI. The people who would normally be most excited about the future are the angriest about it:

I resonate with the backlash.

Within two decades, AI may be orders of magnitude smarter, faster, and cheaper than any human worker, and there will be far more of it than there are of us. The downside scenarios are real.

But none of us get to opt out of the world we live in.

The knowledge workers who don’t embrace AI will be left behind, and no one is coming to bail them out. Those who embrace it will see their productivity shoot up to previously unfathomable levels.

But anger and excitement have one thing in common: neither one tells you what to do next…

The Most Dangerous Career Advice Right Now Is "Figure Out AI"

Everyone agrees AI is transforming the world. Almost nobody agrees on what that actually means for anyone’s career.

A clear, hopeful future has been replaced by fog. Now it’s hard to know whether AI will take all of our jobs in five years or just keep making us more productive and creative for the foreseeable future.

Therefore, it’s hard to know exactly what to do now. It’s hard to know which AI skills will pay off for years, and which will be obsolete by the time you finish learning them.

Many people are falling into one of two camps:

• Opting out of staying on the AI frontier and burying their head in the sand.

• Flailing around trying to do everything. Staying on top of the latest tools, AI models, AI harnesses, prompting techniques, and industry news. Working harder than ever, but not sure if they’re making real progress.

This article is about clarity.

It provides you with the one AI skill and the one category of tools that are virtually guaranteed to deliver the biggest return for knowledge workers who apply them.

This clarity is critical because once you know what won’t change, you know what to invest in now and can be confident it will pay off.

Not only that, based on Harvard research and early results of people who are making the switch (more on this later), I can confidently say that the skill you’ll need to learn is one that you’ll actually enjoy doing.

And if you haven't started yet, that doesn't mean you're behind. You're in Stage 2 of a 5-stage pattern. The window is still open.

Very few have felt the true magnitude and speed of what’s happening, because we’re all inside it. It’s so ever-present that it’s invisible. To really see it, you have to step outside of it.

This article will help you take that step outside.

The Multi-Century Pattern That Reshaped Civilization Twice Is Running A Third Time

In 1700, saying “I can’t read” carried no stigma. By 1900, it was a serious liability.

In 1990, “I don’t use computers” was a defensible position. By 2015, it ended careers.

Today, “I’m not a programmer” is normal. By 2030, it will sound the way “I can’t read” sounded in 1900.

I’m not predicting this flippantly.

We’re inside the third run of a historical pattern that has already reshaped civilization two and a half times:

1. Once for reading and writing (text literacy)

2. Once for counting and calculating (numerical literacy)

3. Finally, for using and authoring software (software literacy)

Just as reading literacy spread for centuries before writing literacy did, software usage (digital literacy) spread for decades before software authorship became mainstream.

Today, knowledge workers use dozens of software apps on their phones, in their browsers, and on their desktops. Someone who can’t use software is essentially unemployable as a knowledge worker.

Starting in November 2025, when AI agents became able to reliably create working code, we entered the second stage of the third literacy—Software Authorship —in which domain experts turn what they know into running systems using plain English, with AI doing the technical work.

On the surface, Software Authorship doesn’t sound like a civilizational shift on the scale of reading or arithmetic. For 50 years, software has been a niche specialty: built by highly paid engineers, used passively by everyone else. Calling it the next universal literacy feels like a stretch at first.

It isn’t.

On the surface, Software Authorship doesn’t seem likely to have much impact on the average knowledge worker’s day-to-day work and career trajectory.

It will.

On the surface, Software Authorship feels like the type of shift that will take decades to run its course.

It will likely take 5-10 years. Maybe less.

The shift will create a new generation of economic winners and losers:

• Winners: On the one hand, the most advanced AI users creating software will be 100x, then 1,000x, and then 10,000x more productive than the average knowledge worker who lightly uses AI in chat. This is already happening, which I explain later in the article.

• Losers: On the other hand, many people will be left behind. Way more and way faster than in any previous technological shift.

I see the tsunami coming, and 99% of people don’t recognize what’s about to happen. As a lifelong educator, my mission is to help people make this shift as smoothly as possible.

On a personal level, I feel motivated by both the opportunity and the risk:

• Over the past few months, I’ve seen my productivity skyrocket higher than it’s been at any other point in my career.

• I fear being left behind on a visceral level because my mom isn’t the only person I’ve watched be left behind in the past, and it’s brutal…

What Happens To The People Who Sit This One Out

A 2005 comment from a dear mentor still sticks with me:

“I used to be good at computers in the 80s. I shouldn’t have let my skill slip. Don’t make the same mistake I made.”

Earlier in his career, my mentor decided to be more productive by delegating technical tasks to his employees rather than learning them himself. Until one day, he woke up and realized four harsh truths:

1. He was completely dependent on others for basic tasks.

2. He had become the kind of person who got the warm handshake at the front of the room and the knowing look behind his back among employees.

3. He was losing contracts to others because he wasn’t keeping up with the times.

4. He was so far behind that he couldn’t catch up.

I remember one moment in my early 20s when he asked me for help with a very basic tech task. It felt so obvious, I couldn’t help but smirk. He paused, closely examined my face, and then immediately ended the interaction. He never asked me for tech help again.

Looking back, I see his vulnerability in asking for help and his shame at my response. I wish I could’ve responded differently.

He never did catch up.

My mom and my mentor weren’t stupid or lazy. On the contrary, during their careers, they were each ambitious and successful. They just didn’t develop a key universal literacy when they had the chance. And they didn’t realize what they’d lost until it was too late.

It’s the boiling frog problem. The water heats one degree at a time. The frog never feels the moment when it should jump out, until the moment when it can’t. AI is doing the same thing to most knowledge workers. Each new headline is interesting but not alarming. Each week, it still feels okay to start later. The water just gets a little hotter.

Realizing this, I started doing the one thing my mom and my mentor didn’t. In March 2023, I made the decision to focus on studying and writing about AI full-time.

Then last winter, my news feeds blew up…

I Spent Twenty Years Convinced I Wasn’t A Programmer. I Was Wrong.

I saw the most luminary programmers stop writing code all at once:

Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now.
—Andrej Karpathy (former head of AI at Tesla), 40,000 likes

Pretty much 100% of our code is written by Claude Code + Opus 4.5. For me personally it has been 100% for two+ months now, I don’t even make small edits by hand. I shipped 22 PRs yesterday and 27 the day before, each one 100% written by Claude.
—Boris Cherny (head of Claude Code) on behalf of Anthropic’s team, 7,000 likes

programming always sucked. it was a requisite pain for ~everyone who wanted to manipulate computers into doing useful things and im glad it’s over. it’s amazing how quickly I’ve moved on and don’t miss even slightly. im resentful that computers didn’t always work this way. I 100%, I don’t write code anymore.
—roon (prominent OpenAI engineer), 6,800 likes

The era of humans writing code is over. Disturbing for those of us who identify as SWEs [software engineers], but no less true. That's not to say SWEs don't have work to do, but writing syntax directly is not it.
—Ryan Dahl (creator of Node.js), 20,000 likes

I also saw headlines like this:

Before, AI would get you 80% of the way there, but you still had to manually fix the last 20% of bugs. Suddenly, people were saying that AI was doing all of their coding.

I knew I should try building software.

I was even excited by the idea.

At the same time, it also filled me with dread because I had already failed before, over and over.

My mom was a computer programmer. She encouraged me to follow that path. So in my teens, I bought the website design books and learned HTML and Adobe Photoshop.

Emboldened by my progress, I bought more advanced programming books and took a computer science course at school. But that’s when I hit a wall. Every time I pushed past the basics, the same cycle started:

• I’d write something

• It wouldn’t work

• I’d spend the next three hours figuring out why

Fix one bug, start building again, hit another.

Most of my time went to debugging. It wasn’t fun.

There was no dramatic moment where I quit. I just gradually stopped trying and made a quiet decision about myself: I’m not a programmer.

After college, I tried the other route to creating software: hiring coders. I found an overseas development team and spent $40,000 over a year, working nights and weekends, building an app that let people track their goals.

I quickly learned what it feels like to depend entirely on someone else to build what’s in your head:

• Because of the 12-hour time zone difference, one miscommunication cost a full day.

• I couldn’t tell whether a fix should take an hour or a week.

• I had no way to tell whether the work was high quality.

It was like bringing your car to a mechanic when you don’t know how a car works. You hand over the keys, you pay the invoice, you hope you weren’t lied to, and you hope your car works when you get it back.

Ultimately, the app failed.

Eventually, I lost interest because the whole process killed everything that made the idea exciting.

By the time AI coding tools arrived, I’d seen the data: virtually every successful software company has a technical founder.

At the same time, I said to myself, “I’m not a programmer. I’m a writer.” I’d made peace with it.

So, when Claude Code launched in 2025, my first reaction was: even if AI writes 95% of the code, I don’t want to spend all my time fixing the other 5%. Even with a shorter learning curve, not worth it.

Then came December 2025, when I saw everyone saying that AI could do 100% of coding. Even though I was interested, I didn’t make time for it. I was busy.

What finally broke through was a friend who sat me down in January and said, “I think you can do this. Let me just show you.”

In one call, he walked me through the basics and suggested a few things to try. For the first time in twenty years, I felt like the “programming door” might not be completely closed.

I tried it. And it wasn’t what I expected.

For my first real project, I decided to create a mental model manual.

I had spent four years creating these manuals by hand for my Mental Model Club. Each one took roughly 50 hours of research, writing, and editing. To start, I downloaded all of the old manuals onto my computer. Then, I asked Claude Code to analyze the structure of each manual. Finally, I asked it to produce a new one in the same structure.

Within a few minutes, I had created a manual on the Second Order Effects mental model. On my very first attempt, it was shockingly close to what took me a month to create manually.

That’s when the lightbulb hit me.

Over the next week, I created 300 more manuals with AI. Same depth as when I did it by hand. But way faster. The numbers didn’t lie:

• Before AI: 192 weeks to create 48 manuals

• After AI: 1 week to create 300 manuals

That’s an astounding 1,200x multiplier. And it had only cost me $50 (on top of my $200/month subscription).

Then I asked Claude Code to build something more complex. And it did. But the things I had to fix were never the code. My time was spent on:

• Deciding what to build

• Planning it out

• Iterating with the AI

• Judging what “done” looked like

For the first time, building software required my expertise, not someone else’s.

And because I wasn’t trapped in debugging hell anymore, something unexpected happened: I was having fun. Not in a forced way. Genuine fun.

I described what I wanted in plain English, and Claude built it. I didn’t write a single line of code. I didn’t read a single line of code.

The systems I’d dreamed about for years, but never had a way to build, were suddenly real. And building them turned out to be the most direct path to everything I’d wanted to do with my work.

After I built one tool, I built another. Then five. Then 20. Then dozens more. In just a few months.

Every one of those tools encodes my expertise in ways that no software company would ever productize, because the knowledge is mine. A 27-step news analysis pipeline built on years of mental models I’ve developed. A writing voice system that captures my exact style. An AI-powered research system with over 12,000 notes searchable by meaning, not just keywords.

None of it required me to be a programmer. It just required my domain expertise and my AI prompting expertise.

After months of spending most of my day programming, I noticed two things that surprised me.

First, I realized I was no longer just a thought leader who happened to program part-time. I was actually a programmer. For example, to generate articles like this one, I spent most of my time developing software to streamline the process. Within a few months, I went from not knowing how to code to identifying as a software engineer. It has been the fastest identity shift I’ve ever gone through in my life.

Second, I realized that I actually love programming now…

The Harvard Research That Explains Why I Was Wrong About How I’d Feel About Programming

There’s a Harvard psychologist named Daniel Gilbert who studies something called affective forecasting: our ability to predict how we’ll feel about experiences we haven’t had yet. His finding, across decades of research, is that we’re terrible at it.

We consistently overestimate how much we’ll hate many things we’ve never tried.

Gilbert’s lab has shown this across romantic breakups, tenure denials, election losses, and dozens of other events people are sure they’ll never recover from. They almost always recover faster than they predicted.

This TED Talk clip summarizes the research:

And, according to a follow-up study, the single best predictor of how you’ll actually feel?

Asking people who’ve already done it.

In the study, Gilbert and his collaborators asked undergraduates to predict how much they would enjoy a 5-minute speed date and a peer evaluation.

• One group got detailed information about the event itself.

• The other group got just one stranger’s reaction to the same experience.

The strangers’ reactions won.

People who relied on a single secondhand report predicted their own feelings more accurately than people who studied the situation in detail and then predicted how they would feel.

And the kicker: when participants were given the choice, they preferred the detailed information. They actively rejected the strategy that worked.

This research is relevant right now because most of the people who move to coding with AI actually enjoy it. Boris Cherny, the creator of Claude Code, says that this is roughly what he sees at Anthropic among people who make the shift. And Anthropic is at the leading edge of this wave.

Furthermore, Lenny Ratchitsky, who interviewed Cherny, found something similar when he did three polls on X that collectively got 1,500+ responses:

Below are the specific poll results:

If Gilbert’s research holds and the trend continues, most people who switch to AI programming will enjoy it.

So the emotional barrier is probably lower than you think.

But there's a second barrier most people haven't questioned yet: the assumption that building software requires a different kind of thinking than you’re already using.

It doesn’t.

Your Job Is Already Software. You Just Don’t See It Yet.

Let me show you what I mean.

Strip away the job titles, and every knowledge worker is doing the same three-step loop all day:

• Input. Take in information.

• Transformation. Make sense of it, process it, create something.

• Output. Export the result.

For example:

• A lawyer takes in the details of the case, drafts the argument, and files the brief.

• A marketer takes in funnel data, develops the angle, and ships the campaign.

• An accountant records transactions, prepares the reconciliation, and sends the report.

• A designer takes in references, develops the direction, and ships the layout.

Every one of those workflows IS fundamentally like software. Information in. Transformation in the middle. Information out. The shape of every knowledge worker’s job is the shape of a software workflow.

If you’re a consultant or a coach or a strategist and you just felt your jaw tighten at the idea that your job is “fundamentally like software,” I get it. I had the same reaction. My work felt too human, too intuitive, too judgment-dependent to be described that way.

But only the shape of the work is software. The soul of the work is domain expertise.

Looking at the full sweep of knowledge work, four distinct eras emerge, each one inverting the relationship between human and software a little further:

1. Era 1: Human With Mechanical Tools (before ~1980). Work happens entirely in the human’s head and hands with mechanical tools. Paper, pens, ledgers, typewriters. Software doesn’t exist as a workplace tool.

2. Era 2: Human With Software (~1980 to present). The human is the agent. Software is the tool. The human does the work, and the software helps along the way. The lawyer types in Word. The accountant works in Excel. The marketer logs into HubSpot. (This is where most of us still are. We've spent our entire careers getting very, very good at being the human in "Human With Software.")

3. Era 3: Software With Human (2024 to present). The inversion happens. Software does the work. The human directs and judges. The founder doesn’t write the cold outreach. She designs a system that writes thousands of personalized messages while she sleeps. She isn’t using software the way her predecessors used Outlook. The software is doing the work. She’s directing it.

4. Era 4: Software-Only (~2030+). Software does the work autonomously without human direction in real time. Already true in narrow domains: algorithmic trading, automated support for routine cases, dynamic pricing engines, ad bidding. Likely to expand as Era 3 systems mature.

We are standing at the line between Era 2 and Era 3 right now. The professionals who have already crossed it are building 20x leverage. For example, serial entrepreneur Garry Tan is literally coding 400x faster than before AI:

Source: Y Combinator Podcast

Although Tan is an outlier because he’s an early adopter and world-class software engineer, he’s not alone. Legendary entrepreneur and investor Marc Andreessen reports that programmers in his portfolio company are 20x more productive with AI. This experience also aligns with my daily experience and that of friends who are all in on the tools.

I now believe that, in a year or two, people still operating with an Era 2 mindset (humans with software) will watch their work get done around them by people with a fraction of their experience.

The skill you need to move from Era 2 to Era 3 (software with humans) is Software Authorship: the ability to turn what you know into running software, using English as the interface and AI as the implementation. Not writing code. Just describing what you need, precisely enough that AI can code for you.

Boris Cherny is the head of Claude Code at Anthropic, the tool I use to build my own software. He stopped writing code entirely in November 2025 after AI became good enough to write it for him. But the most important thing he’s said isn’t about coding is about who builds the best software:

“The best person to write accounting software, I think maybe even today, is not an engineer. It’s a really good accountant because they know the domain really well. And coding is the easy part. It’s knowing the domain that’s the hard part.”

The technical part is now the easy part. The knowledge you’ve spent your career developing is the hard part. And “hard” here means “valuable.” It means “irreplaceable.” It means that the person who knows a domain most deeply builds the best tools, because the tools are made of domain knowledge now, not code.

This is why I’m saying that almost every knowledge job has software-shaped holes in it that better software would fill:

• The reports your team runs that take hours to compile.

• The data hand-offs between systems your IT department keeps promising to fix.

• The dashboards you wish you had.

• The custom tools that would make your job 40% easier, if only somebody would build them.

The only reason these tools don’t exist is because the supply of programmers has always been tiny relative to the demand for software.

That supply constraint just broke. Because now it’s possible for anyone to build.

The Five-Stage Pattern That Has Reshaped Civilization Twice Is Running Again

Everyone is asking, “Will AI replace me?”

That’s the wrong question for this moment.

The real question is bigger and older: what happens when a skill that only specialists have becomes something everyone can do?

That question has been answered exactly twice in human history. Both times, the answer unfolded as a five-stage pattern that reshaped economies, professions, and daily life. I call that pattern the Literacy Arc.

My mom and my mentor didn't see the Literacy Arc until it was too late. The rest of this article lays out the full pattern so you can see where you are right now and respond better: the five stages, the two forces driving them, the historical precedent, and the specific window that will be open for the next few years.

READ THE REST OF THE ARTICLE

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Now let me show you the full pattern…

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From Research to Blockbuster Ideas: Using AI to Generate Transformative Thought Leadership

Most people think great thought leadership starts with writing. It doesn’t. It starts with ideas — and most people are generating ideas the wrong way.

They fixate on one idea too early. They skip the research. They jump straight from a vague prompt to an article and wonder why the output feels flat. What actually separates a piece that gets 92,000 shares from one that disappears is a disciplined, systematic process — and AI has made that process available to anyone willing to learn it.

This is what the Seminal course is built around: not just how to use AI to write faster, but how to use it to think better. To find ideas that are genuinely counterintuitive, that carry real intellectual weight, and that position you as a thought leader with a distinctive point of view — not just another voice adding to the noise.

In this session, we went deep on the bridge between research and id…

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AI-Powered Research for Blockbuster Content

Most writers treat research as a necessary chore — something you do to support an argument you’ve already decided to make. I’ve spent my career doing the opposite, and it’s been my single biggest competitive edge.

When I was writing regularly for Medium, I spent 80% of my time on research. Not to cherry-pick evidence, but to genuinely understand a topic well enough that original ideas became obvious. That’s a different thing entirely. It’s the difference between writing that sounds credible and writing that actually is credible — work that holds up, that readers return to, and that can’t be easily replicated by someone who just skimmed the surface.

What AI changes is the scale at which this is now possible. The research that used to take weeks can now be done in hours. More importantly, AI doesn’t just help you do research — it becomes the user of that research, able to synthesize and generate from a knowledge base that wou…

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Most people treat AI like a search engine with a chat interface. They paste in a question, get an answer, and move on. That approach will always hit a ceiling—because it puts you as the bottleneck, manually copying and pasting between screens, losing context, and starting over every session.

The Blockbuster Brain is a different paradigm entirely. It’s a living, queryable system built on your data—your podcast clips, your articles, your Zoom transcripts, your notes—that gets smarter the more you feed it, and can run complex operations autonomously while you sleep. In this class, I showed participants exactly how I’ve built this system for myself using Claude Code, including the mistakes I made spending 100+ hours on the wrong approach and how a single tool changed everything.

This is part of a broader course on using AI not just to work faster, but to think better. If you’re serious about staying ahead of how knowledge work is changing—and it is changing faster than most people realize—this is the work that matters.

During this class, we:

• Explored why traditional second brains fail and what makes AI different

• Walked through the full Blockbuster Brain architecture I use daily

• Compared AI-augmented learning vs. full automation—and the tradeoffs of each

• Saw a live demo of Claude Code building automations in real time

• Discussed how creativity actually works and why a broad knowledge base is the key input

• Learned why Claude Code is fundamentally different from chat-based AI tools

• Explored the “Third Brain” concept: a repository of cognitive operations, not just notes

• Looked at how to build a queryable knowledge layer on top of raw source files

• Covered how to extract hidden expertise from your own transcripts and conversations

• Answered participant questions about setup, tool choices, and where to start

Implications

The shift I’m describing isn’t just a productivity upgrade—it’s a structural change in what knowledge work even means. For most of history, the limiting factor in creating valuable ideas was access to information and the time to process it. Both constraints are dissolving faster than most institutions or individuals have reckoned with.

What this class points to is a world where the quality of your thinking is increasingly determined not by how hard you work, but by the architecture of the systems you’ve built around your mind. The people who understand this now—who take their accumulated knowledge, their frameworks, their hard-won expertise, and encode it into systems that can query, recombine, and surface insights on demand—are building a compounding advantage that will only grow.

The deeper implication is philosophical: creativity, as researchers like Robert Weisberg have documented, isn’t magic. It’s combinatorial. The broader and more diverse your knowledge base, the more raw material your mind—and now AI—has to work with. Building a Blockbuster Brain isn’t about outsourcing your thinking. It’s about expanding the surface area of what’s possible to think.Discussed the one action participants should take before the end of next week.

AI-Generated Podcast Summary Of The Class

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Said differently, paid members get access to 18 hours of learning for just $20/month or $149/year. This comes to just $8 for every hour of live class. And this doesn’t even include our other live monthly class, Augmented Awakening, or over $2,500 in other perks (20+ prompts, 7 courses, 3 books, blockbuster article library, etc).

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RECORDING RESOURCES

1. Presentation Slides

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3. Class Transcript

4. Other Classes In The Blockbuster Live Course

5. Resources Shared

6. AI Timestamps

7. AI Chapter Summaries

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Yet, my own teenage daughter thinks I’m on the wrong side of history.

She isn’t a Luddite either. She’s just looking around as:

• Her generation gets hollowed out by social media addiction

• The same tech industry promises it’ll save us with something more powerful.

• Stories of sexual deepfakes of women and children spread.

• Her peers use AI to cheat their way through school.

• Environmental damage from data centers is increasing.

• AI companies illegally steal the intellectual property of creatives.

• The future job market disappears faster than anyone is willing to admit.

My son isn’t angry like she is. He’s indifferent. The technology that’s reorganizing the global economy doesn’t seem worth his attention.

I assumed I’d talk them out of it.

And boy did I try.

I opined on the opportunities, the ease of getting started, and the importance of moving early.

But the more I talked, the more I realized I was the one being lectured. My daughter wasn’t repeating talking points. She was describing a world she lives in that I don’t.

What I now see is that they have grown up in a completely different reality from mine.

When I was a teenager, I read inspiring magazine cover stories about entrepreneurs like Steve Jobs, Richard Branson, Bill Gates, and Jeff Bezos.

These stories of young people making it big and changing the world inspired me to co-found a business.

My kids grew up with the inverse:

• Elon Musk’s rants on X.

• Allegations about Bill Gates and Jeffrey Epstein.

• A decade of tech in the form of social media rotting their friends’ attention spans.

• A generation of parents losing middle-class footing despite doing everything right.

• Mind-boggling wealth concentration:

For me, the arrow of the future pointed up. For my kids, it points into oblivion. A fast-changing alien future no one can see.

So I stopped trying to convince them and started trying to understand them. I spent the next several months reading the research on AI and young people.

What I found is worse than I expected. And it’s worse than almost anyone in my industry is willing to say out loud.

This essay is what I learned, why I think the people building AI are systematically refusing to look at it, and what my daughter saw that I didn’t…

My Kids Aren’t Alone

A quote from an April 2026 Gallup poll of 1,500 young people captures the situation:

“Gen Z’s sentiment toward AI has become significantly more negative…”

This table, based on the poll, shows the magnitude of the one-year sentiment shift:

These stats are astounding when you think about them…

• Young people are typically the most excited about new technologies. This generation isn’t.

• As AI gets better, you’d think young people would expect AI to make them more productive. They think the opposite.

• These effects could skyrocket as we’re still at the beginning of the AI explosion.

After doing a lot of research, I now believe two seemingly opposite ideas are both true:

1. The near future will be awesome for select young people. The 5% most entrepreneurial young people will do better than any previous generation. They will learn AI on their own and build bigger businesses faster. In a fascinating tweet, the CEO of Y Combinator, the largest startup accelerator in the world, recently shared that 3X more companies in their latest 3-month batch hit $1M in annualized revenue compared to the previous cohort.
   

2. The near future for most young people will suck. The 95% who aren’t entrepreneurial or going into blue-collar work will have a very tumultuous and uncertain beginning to their career. Many will graduate with record college debt and a cost of living that’s higher than ever. 64% will move back in with their parents.

These two groups seem to be completely talking past each other.

Every single AI podcast I listen to enthuses about the immense opportunities for young entrepreneurs. Sam Altman says there has never been a better time for a young person to start a business.

Yet, what is left unacknowledged is that only a minuscule percent of the population has the desire and inclination to actually start a successful business—even with entrepreneurship education. And, I should know because I spent most of my career in that field.

But before I jump deeper into the research, it’s critical to understand what’s at stake here for us all…

Why Young People And AI Matter

“The future is already here—it’s just not evenly distributed.”
—William Gibson

The future doesn’t happen overnight out of nowhere.

It happens gradually in the hidden fringes as small trends go mainstream.

Therefore, one of the most reliable ways to make educated predictions about the future is simply to find where the future is already happening.

Coding is the canary in the coal mine. It's the field AI has hit hardest and earliest, and the patterns showing up there now will spread across knowledge work within a year. Watch the canary, and you get a year's head start on your own field.

Similarly, young people are a canary generation.

They give us our first glimpse of how AI may impact all knowledge workers over the next 3-5 years.

More so, the implications go beyond economics into politics.

In 2010, complexity scientist Peter Turchin published a viral study in Nature. He used a mathematical model of state collapse based on historical data to predict that the United States and Western Europe were heading into a decade of political instability around 2020. He was right. And the variable he identified as most predictive wasn’t inequality, or polarization, or media fragmentation. It was the “overproduction of young elites.” He particularly made this point in his 2023 book, End Times:

“Overproduction of youth with advanced degrees has been the most significant factor driving societal upheavals, from the Revolutions of 1848 to the Arab Spring of 2011.”

Given these stakes, the research findings so far are particularly important to pay attention to…

The Research Is Already Here, and It’s Troubling

In August 2025, Erik Brynjolfsson, the Stanford economist, published what may be the most important empirical paper on AI and employment to date. The study, Canaries in the Coal Mine?, uses payroll data from ADP covering millions of workers across tens of thousands of firms.

Since AI tools became widespread, companies have sharply cut hiring of young knowledge workers:

While experienced workers in the same roles have been largely unaffected:

There’s one important caveat, though.

Brynjolfsson’s paper is careful to note that post-pandemic tech hiring corrections, interest rate changes, and the unwinding of 2021-2022 overhiring all played a role in the drop, too.

So he conducted a follow-up study that tested alternative explanations (interest rates, post-pandemic correction, tech overhiring) and found that the AI effect only grew stronger.

Bottom line:

AI is adding intense pressure to young knowledge workers.

Despite this pressure on an entire generation, there is something odd that’s making it easy to ignore…

The Quiet Crisis

This emerging crisis did not arrive in a televised crash, or a market panic, or a single announcement from a large company saying the old bargain was over. It has arrived more quietly:

• A job posting did not appear.

• An internship class got smaller.

• A manager decided not to hire a new person after another retired.

From inside, the situation feels stable. If you already have a good job, your paycheck arrives. Your team is busy. The economy appears healthy. But if you are trying to enter the workplace, the same condition feels like a wall.

A full hotel looks successful to the owner and hopeless to the person outside with a suitcase.

Young people are the ones with the suitcase.

Not only that, the crisis is quiet on another level, because of how the metrics are measured.

Let’s say a recent computer science grad applies for lots of programming jobs, but doesn’t get hired. As a result, she decides to work in retail while applying for more software roles.

The data says she is employed.

It doesn’t say she is an unemployed programmer.

Multiply her situation by hundreds of thousands of more young people.

Bottom line:

Our statistics are based on the jobs people have held. They’re much less good at measuring the jobs people were prevented from starting.

How AI Breaks The Bottom Rung Of The Career Ladder

When I first saw the Brynjolfsson data, I thought the story was just about AI replacing junior workers.

That’s how most people are framing it.

But there’s something more profound happening here.

Historically, new technologies have hit experienced workers hardest. A study from MIT Sloan and Northwestern’s Kellogg School that tracked technology displacement across occupations from 1981 to 2016 found that when new tools could perform tasks in place of workers, all affected workers suffered wage losses. Younger workers, with less invested in now-obsolete skills, typically adapted faster.

But Brynjolfsson found that experienced workers in the same AI-exposed occupations didn’t decline. They stayed stable, or grew. This moment is unique in tech history because AI is replacing beginners, not veterans. It’s amplifying experiential expertise.

And the implications for all of us are more alarming than most people realize.

Here’s how work used to function:

• Step #1: Junior workers supplied codified knowledge. From college, they knew the rules, methods, syntax, research process, spreadsheet techniques, case law search, and coding patterns. They weren’t yet wise, but they could produce useful first passes.
  

• Step #2: Junior workers gain judgment via experience. That usefulness bought them a seat inside companies where they could observe experienced people making real decisions. Over time, through that proximity, they acquired something much harder to teach: judgment.

Simple. Right?

Here’s the issue, though:

AI replaces the codified knowledge of young people. It can draft, summarize, classify, scaffold, translate, synthesize, generate options, and imitate formats.

AI magnifies those with experience. Senior workers know what questions should be asked. They know what answer is suspicious. They know when a technically correct solution fails socially, legally, operationally, or morally. They know which facts matter and which are decoration.

Brynjolfsson puts the situation plainly in an interview with Derek Thompson:

“LLMs learn from what’s written down and codified, like books, articles, Reddit, the internet. There’s overlap between what young workers learn in classrooms, like at Stanford, and what LLMs can replicate. Senior workers rely more on tacit knowledge, which is the tips and tricks of the trade that aren’t written down.”

That’s the mechanism. And it creates a reinforcing loop with no natural brake:

• Better AI.

• Senior employees become more productive with AI.

• Fewer juniors are needed.

• Fewer juniors are hired.

Bottom line:

AI isn’t replacing experienced workers. It’s replacing the training process that creates them.

At which point, a problematic situation activates.

Entry-level work was never just about production. It was the mechanism by which tacit knowledge was transferred across generations.

The senior engineer didn’t just produce output. He was also training the next generation. Therefore, when firms cut junior hiring, they’re cutting their own training function. They just don’t notice it yet, because the training was invisible. It was embedded in the work itself.

I have on multiple occasions produced output with AI that I would have previously estimated would take weeks of work. I am the direct beneficiary of the productivity amplification that feeds the loop. That’s exactly why I find it so alarming. Because I can feel, from the inside, how rational it is for every individual organization to do this. And how catastrophic it becomes when everyone does it simultaneously.

The market sees today’s productivity. It does not automatically price tomorrow’s missing apprenticeship.

We are removing the tasks that train judgment because AI can perform the tasks before judgment forms.

The senior employees who hold that judgment today will eventually retire. And when they do, we will discover what it costs to have skipped a generation of training.

Every Door Closed at Once

Previous automation waves were painful but more navigable.

• Factory automation closed manufacturing entry-level jobs, but opened IT, maintenance, and service positions.

• Computerization eliminated clerical roles but created software, networking, and digital marketing jobs.

In every case, one set of entry points closed while another set opened.

There was always somewhere else to go. The transition might have required retraining, relocating, or starting over at the bottom of a new, promising field. But at least the new field existed.

AI will certainly create new jobs. But AI is doing something no previous technology has ever done. It’s closing entry points across most types of knowledge work simultaneously.

The same technology that automates junior coding automates junior legal research. It automates junior financial analysis, junior marketing, junior journalism, junior consulting, and junior administration. The entry-level tasks across all of these fields share the same characteristics: they’re relatively well-defined, have clear success criteria, require broad but shallow knowledge, and produce outputs that can be evaluated by more experienced humans.

This is precisely the kind of work at which current AI excels.

No previous automation wave has ever moved this fast or hit this many industries at once, while there were so few new jobs to turn to.

And the cruelest part? The social contract around education is breaking at the same time.

For decades, the deal was simple:

1. Go to college

2. Acquire skills and credentials

3. Get a professional job

4. Pay off your loans

5. Build a life

That contract is embedded in every aspect of a child’s life:

• Parental expectations.

• High school guidance counseling.

• Student loan structures.

It is the entire architecture of how we organize the transition from adolescence to adulthood.

AI is breaking this contract in a very specific and damaging way. It simultaneously reduces the value of credentials while maintaining credential requirements and increasing credential costs.

A computer science degree means less as a hiring signal because AI can write functional code. But companies still require it, because hiring practices change slower than technology.

And right now, student debt in the United States exceeds $1.7 trillion. To put that in perspective: it’s more than the GDP of Australia. An entire wealthy nation’s worth of economic output, owed by young people who took on debt to buy access to a middle-class life.

Bottom line:

A 22-year-old graduating with a computer science degree and $87,000 in student debt is not a symbol of irresponsibility. She is a symbol of obedience.

She did the thing the culture asked of her. She chose the hard major. She listened when adults said the humanities were risky and technical skills were safe. She accepted debt because the debt was supposed to purchase access to the future.

That is what makes the story morally difficult. The advice was not stupid when it was given. In the world of 2018 or 2020 or even 2022, studying computer science looked like one of the most rational decisions a young person could make.

The world changed faster than the advice.

Now, instead of saying “Learn To Code”, the advice is “Learn AI.”

While I think this is good advice for professionals today, I’m not sure it’s the most practical advice for a 10-year-old. When that young person eventually graduates from college in 2038, AI might be doing almost all knowledge work faster and smarter than a human can even keep up with. 12 years in AI time is like 100 years in normal time.

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When a Byzantine princess used one at her wedding in 1004, a clergyman denounced her for “excessive delicacy.” According to a historian, when she died of plague two years later, the Church called it divine punishment.

When Henry III of France used one in 1575, his courtiers sneered: “Of course you would. You dress like a woman.”

When an English traveler brought one back from Italy in 1608, his friends gave him a new nickname: Furcifer, a Latin insult referring to his new habit.

What were they all being mocked for?

Eating with a fork!

For 600 years, Europe’s elites dismissed the fork as a mark of vanity. Queen Elizabeth felt it was crude and preferred to eat with her fingers. Church leaders said it looked like the Devil’s pitchfork.

Every era found a new reason to reject it.

Yet today, billions of people use one every day without thinking.

The Thing You Mock Today May Be Your Biggest Opportunity Tomorrow

We see this stigma-to-success pattern throughout history:

• Tattoos were a mark of sailors and convicts before kindergarten teachers and baristas made them mainstream.

• Therapy was something you only admitted to if you were “in a dark place” before it was what every high-achiever scheduled.

• Working from home was for slackers who couldn’t hack a real office before it was the default arrangement for the most productive knowledge workers in the economy.

• Meditation was hippie-monk weirdness your coworkers would quietly mock before it was the Calm app every executive opens before a board meeting.

Each shift begins the same way. A small group tries the thing. The majority mocks them. Years pass. The mockers quietly start doing it too, denying they ever mocked it, and the practice gets a new respectable name.

The writing is always on the wall, and almost no one reads it.

If you’re a creator or entrepreneur, this pattern is particularly worth understanding. At a fundamental level, you create value by doing or saying things that are useful when others can’t, won’t, or haven’t.

However, to fully capitalize on the phenomenon, you need answers:

• Why do some things go from stigma to success while others don’t?

• What are the predictable steps in the stigma-to-success shift?

• Where are there great opportunities that are overlooked simply because they’re temporarily stigmatized?

• How can you time when the shift will happen?

This article answers all four questions and uncovers one of AI’s biggest opportunities in the process.

The Stigma-To-Success Shift Happens Especially Fast With AI

What took centuries for the fork only takes months or years with AI.

For example:

• In 2022, ChatGPT for professional work was “a fun toy”, before it became table stakes in knowledge work. The people who treated it seriously that year are now years ahead of the ones who waited.

• Two years ago, using AI for research was considered “lazy” and “unreliable” because “AI hallucinates.” The people who used it anyway, learning to verify as they went, are now years ahead of the skeptics.

• Two years ago, using AI for hard conversations (grief, conflict, career decisions) was considered a “sad substitute for real connection.” Today, therapy is the #1 consumer use case of AI.

Right now, we are inside exactly such a moment with AI-generated content…

AI-Generated Content Is At The Inflection Point Right Now

Most people have a fixed mental model of AI-generated content:

• “AI content is slop.”

• “AI content will stay slop.”

• “Anyone producing AI content is lazy.”

• “AI content is contributing to the cultural garbage pile.”

They have receipts too:

• LinkedIn flooded with robotic posts.

• Amazon listings poisoned by AI-generated books citing nonexistent studies.

• AI news sites hallucinating quotes from real people.

The slop is real. No argument there.

But a snapshot is not a trajectory...

The Handcraft Illusion: What you think is handcrafted probably isn’t

Every “AI is slop” argument rests on a silent comparison.

• AI content. Artificial. Mechanical. Mass-produced. The word “artificial” is literally in the name.

• Handcrafted human content. A real writer at a real desk, pulling sentences out of their own mind, each word chosen by hand.

This comparison has multiple hidden assumptions, and pulling on any one of them makes the slop argument collapse. Let’s start with the most obvious one.

How much of what you love is actually handcrafted?

By handcrafted, I mean made by a specific human, one at a time, using traditional tools, the way things were made before the Industrial Revolution. Not “artisan branded.” Actually made by a person’s hands.

Try this thought exercise:

Inventory the next room you’re in. Chair. Desk. Computer. Clothes. Cup. Light fixture. Flooring. Paint. Phone.

How much of what you are looking at is handcrafted?

For almost everyone reading this, the answer is close to zero. Maybe a ceramic mug. Maybe one painting. That is it.

This matters because the objection I hear most about AI content is some version of “I like handcrafted. I like knowing a human made this.”

The feeling is real.

The feeling also only governs about 4% of our actual buying behavior.

The other 96% shows up on your Visa statement. Mass-manufactured clothes from a global supply chain. A computer assembled by robots in Shenzhen. Cutlery stamped from sheet metal at four thousand units per hour. Even the local farm eggs came in packaging mass-produced in an Ohio factory.

Handcrafted objects are a story we tell about 4% of our possessions.

We think we live in a handcrafted world. We don’t. We think we prefer handcrafted. We mostly don’t.

This begs a question…

How does this apply to handcrafted human writing like articles, essays, videos, and posts that come from individual human minds?

Surely THAT’s different than handcrafted objects…

Human Content Has A Quality Problem Too

The AI slop argument assumes the human content is never slop.

Wrong.

Human content has been getting worse for at least a decade on multiple levels:

• Creators learned to optimize for the algorithm, which rewarded engagement over depth. Clickbait, rage-bait, and addictive-but-hollow content were the result.

• Ad supply exploded across the open internet. Advertising rates collapsed. News sites that once funded investigative reporting cut budgets, slashed fact-checkers, and replaced depth with volume.

The human content landscape did not just get diluted by amateur entrants. It got actively degraded by the business model that rewarded attention-capture over value.

When people point at LinkedIn and say “look at the AI slop,” they are pointing at a landscape that was already full of human slop. The robotic AI posts are replacing shallow think pieces, not replacing Malcolm Gladwell.

So, using human-created content as the baseline in a direct comparison has two problems. First, most of it is not handcrafted with the same care that everyone assumes. Second, most of it is often not good. Defending human-created slop against AI slop doesn’t fix anything.

Which leaves one real question:

What is the actual alternative to AI and human slop?

The Real Debate: Hand-Crafted vs Hand-Designed

Chef-branded restaurants at scale do not have the chef chopping every vegetable. The chef’s signature is in the system. Their taste, their judgment, their vision shape the experience, even though they didn’t touch most of the food. You accept that their fingerprint is in the design of your meal.

The same pattern is coming to content, and faster than most people think. Here is why.

The top non-fiction creators of the next decade are going to do something that was previously impossible:

• Concretize what makes them special (their topic selection, their voice, their signature frameworks, their research process, their point of view) into an AI system.

• Augment that special sauce using AI: stress-testing their ideas more ruthlessly than any human editor could, catching their own blindspots across hundreds of drafts, surfacing patterns across sources they would never have had time to read, verifying factual claims at machine speed.

• Scale their output. First 2x. Then 10x. Then 100x and beyond.

The chef’s signature stays in every dish, because the system was designed to carry it. The taste, the judgment, the recognizable voice: all present, all amplified.

This is hand-designed content. Not handcrafted. The distinction is the whole argument. The creator’s fingerprints concentrate in the system rather than in the individual keystrokes, and the reader receives the benefit of their taste at a scale that was never reachable before.

Think of it this way.

Rather than the Internet being filled with nameless AI slop, it could be filled with more work from authors you love. Imagine if you got a Malcolm Gladwell, Michael Lewis, or Yuval Noah Harari book every year rather than every five years.

Why “AI Is Slop” Will Age Terribly

The Slop Fallacy is the move from “current AI content is mostly bad” to “AI content will always be mostly bad.” Critics made this same mistake with digital cameras in 1993, online dating in 2005, and electric cars in 2009. In every case, the assessment of the current state was correct. But the extrapolation from current state to future state was catastrophically wrong.

Tech investor Chris Dixon named this pattern in his 2010 essay The Next Big Thing Will Start Out Looking Like a Toy, building on Clayton Christensen’s The Innovator’s Dilemma. Important new technologies look like toys at launch because they can’t do what mainstream products already do well. Critics miss two things that compound:

1. The improvement curve is steeper than they can see from where they’re standing

2. The technology enables uses the existing products cannot reach.

Below are the four most important reasons among many that AI content will not stay slop:

#1. TRAJECTORY:
Today is the worst AI content will ever be

AI is improving faster than almost any technology in history. The improvement is not linear. It’s exponential. What today looks clunky, hallucinating, or generic is a snapshot of the launch state, not the trajectory. Current-state criticism misses where the curve is heading.

#2. PERSONALIZATION:
AI content can be personalized to you specifically

Human content has always been a compromise written for a fictional average reader who isn’t you. The writer aimed somewhere in the middle of their assumed audience, leaving you either patronized or lost.

AI content solves this via personalization on multiple levels:

• Expertise. You ask about a new research paper and get an explanation pitched at your existing expertise.

• Length. You’re in a rush and want the TLDR.

• What You Don’t Know. You ask about a market trend and get the parts you don’t already know, not the introduction you don’t need.

• Goal. You’re reading an article to see if it impacts an article you’re writing. AI extracts just what you need.

• Medium. You prefer to learn with slides rather than text. AI can convert any article into audio, video, or slides.

The quality upgrade from personalization is bigger than critics register.

#3. NICHE ECONOMICS:
AI makes niche content economic for the first time

Vast demand exists for content that doesn’t exist yet because it is uneconomic for humans to produce:

• Hyper-specific podcasts.

• Tutorials for niche software.

• Explainers of obscure research papers.

• Travel guides for unusual cities.

• News that no one else is covering.

Human creators cannot economically reach audiences of 20-2,000 readers. AI can, because it is so cheap to create. Furthermore, AI content won’t compete with human content in the niches. Rather, it will compete against nothing.

What’s also important here is that niche content has built-in demand. People are already searching for it, but there are no relevant results. So when AI content fills the hole, it will rise to the top and be discovered.

#4. RESEARCH DEPTH:
AI can read what humans can’t afford to read

Human journalism has always been throttled by a brutal constraint: a reporter can only cover what they have time to read. Vast troves of publicly-available information — SEC filings, regulatory rulings, court records, legislative text — get covered only at the surface, if at all. Bloomberg covers large-cap filings; nobody covers small-cap bankruptcies. The Times covers famous lawsuits; nobody reads the 3.5 million pages of court documents dumped in the Epstein case.

AI collapses the constraint.

One independent AI creator can do more research than an entire human-only newsroom when the research can be done by reading secondary sources.

The critics are comparing AI content to human coverage that was never going to exist in the first place.

Stack these reasons together, and you are no longer comparing AI content to the best human content. You are comparing personalized-AI-content-that-exists to broadcast-human-content-that-wasn’t-made-for-you-and-isn’t-very-good-anymore. And the number of reasons will keep stacking.

The current “no one wants AI content” consensus will age about as well as the “no one will ever meet their spouse online” consensus of 2003.

The Cultural Pattern Behind The Shift From Stigmatized To Successful

Here’s the mental model for how the culture shift actually works, the one that almost no one has.

This framework builds on three established ideas:

• Everett Rogers’ Diffusion of Innovations (1962) mapped how new practices spread through populations in a predictable curve from innovators to late adopters.

• Timur Kuran’s preference cascades, from his 1995 book Private Truths, Public Lies, explained why cultural shifts look sudden: people hide their real views under social pressure until a threshold breaks and everyone admits at once.

• The Overton window, coined by political analyst Joseph Overton in the 1990s, described how ideas migrate through zones of acceptability, from “unthinkable” to “radical” to “popular” to “policy.”

The six pillars below are what those dynamics look like, specifically when the resistance is identity-linked stigma:

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When I was 16, a book called Unleashing The Ideavirus rewired my brain. Seth Godin said one thing that stuck with me: ideas don’t just happen to spread. They’re engineered to.

That idea sent me on a 25-year obsession.

I spent thousands of hours dissecting why certain ideas break through while seemingly good ones die in obscurity. As a result, I was able to go from a failing blog to publishing articles that have been read cumulatively over 100 million times across Forbes, Fortune, TIME, the World Economic Forum, and the Harvard Business Review.

But my comprehensive approach to reverse-engineering had a cost. On average, a single article took 60+ hours. The method demanded exhaustive cross-disciplinary research and 15 drafts per article. Two tradeoffs haunted every project:

• Quality vs. quantity

• Augmentation vs. automation

I opted for quality over quantity and augmentation over automation. Every article was a chance to follow my curiosity and grow.

I accepted those tradeoffs for a decade.

This year, Claude Code + Opus 4.6 made them obsolete. Now, it’s possible to get the best of both worlds.

This article is the proof.

It was created using an AI thought leadership system that produces high-quality content quickly, at scale. The system that 16-year-old me was actually looking for, even if he didn’t know it yet.

For the first time ever, I’m now sharing that system with a small group of pioneering entrepreneurs and senior executives at $1M+ companies who want it installed for themselves, their employees, or their company. If you’re interested, reply with BLOCKBUSTER in the comments or to this email, and I’ll send you more details.

Now on to the article…

Quick Summary

I believe that the most important AI decision you make this year is to move from the chat paradigm (ChatGPT, Gemini, Grok, Claude.AI) to the agentic paradigm (Codex, OpenClaw, and Claude Code).

I’ve now talked to dozens of people about how they’re making this decision. And there are a ton of misconceptions.

For example:

1. Many think that Claude Code is just for programmers. It’s not.

2. Many feel overwhelmed by all the new tools and think Claude Code is like the others. It’s not. It’s way more important.

3. Many love the Chat paradigm, and they don’t realize the hidden taxes they’re paying and the opportunities they’re missing.

This article provides one of the most comprehensive overviews of the difference between the two paradigms, so you can make your most important AI adoption decision better.

The rise of the agentic paradigm is the new ChatGPT moment, and adopting it sooner will have a profound impact on your life.

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In the 1880s, the electric motor arrived in American factories. The factory owners did exactly what you’d expect. They ripped out their steam engines and dropped electric motors in the same spot. One giant motor in the center of the building, connected to the same system of shafts, belts, and pulleys that had distributed power from steam.

The result? Almost no productivity gain. For thirty years.

Economists Paul David and Chad Syverson documented this lag extensively. Factories got a better power source but kept the old architecture.

To see why that mattered, picture a steam-powered factory.

One massive engine in the basement spun a central shaft running the length of the building. Leather belts dropped from that shaft to every machine on the floor. If a machine wasn’t within reach of the belt, it didn’t run.

That single constraint dictated everything. Machines were arranged by proximity to the shaft, not by the logic of the work. Workers carried half-finished parts back and forth across the room because the layout served the power source, not the product.

Then came the electric motor. And for thirty years, factories just swapped it in — tearing out the steam engine and running the same shaft off a big electric one. Faster, cleaner, structurally identical.

The constraint was gone. The behavior wasn’t.

It took a full generation before Henry Ford realized the obvious. Electric motors didn’t need to be centralized. You could put a small motor on each machine. And once you did, you could rearrange the entire factory floor around the flow of work instead of the flow of power. The assembly line was born:

That’s when productivity exploded. Not when the technology arrived, but when the architecture changed to match it.

As the economic historian Paul David observed:

The productivity gains from electrification achieved their full flowering only after manufacturers ceased trying to adapt dynamo technology to the mechanical drive factory and began constructing factories around the new technology.

Think about what David is really saying here. The technology wasn’t the bottleneck. The mental model was. Factory owners kept trying to make a revolutionary technology fit inside a pre-revolutionary structure, and then wondered why it didn’t feel revolutionary.

This pattern didn’t just happen once. It happens every time a revolutionary technology meets an old way of working.

What’s going on here?

Why do smart people keep making this same mistake?

And what does it have to do with how you’re using AI right now?

The Pattern That Keeps Repeating

The factory story isn’t unique. The same pattern (revolutionary tool, old architecture, disappointing results) repeats across every domain I’ve studied...

Exhibit #1: Personal Computing.

The first spreadsheet users treated VisiCalc like a faster calculator. They’d compute one cell, write down the answer, clear the screen, and compute the next. It took years before people realized the power wasn’t in any single calculation. It was in the connections between cells. The same data updated everywhere simultaneously. The spreadsheet wasn’t a better calculator. It was a different category of tool entirely. But only if you stopped using it like a calculator.

Exhibit #2: Photography.

When digital cameras first appeared, professional photographers used them exactly like film cameras. One shot, careful composition, review later. They didn’t exploit the fact that digital eliminated the cost of experimentation. It took a new generation of photographers, people who’d never internalized the “film is expensive” constraint, to discover that the real advantage was shooting thousands of frames and finding the one that captured something no amount of careful composition could have planned.

Exhibit #3: Medicine.

When electronic health records replaced paper charts, most hospitals just digitized the paper. Same forms, same workflows, now on a screen. The result was that doctors spent more time on documentation, not less. It wasn’t until systems were redesigned around what digital made possible (shared records, automated alerts, pattern detection across thousands of patients) that the technology delivered on its promise.

Exhibit #4: Music Production.

When digital audio workstations replaced analog tape, the first generation of producers used them as better tape machines. Record, edit, mix. Same linear workflow. The breakthrough came when producers realized digital audio could be non-linear. You could remix, layer, and restructure infinitely without degradation. That insight created entirely new genres of music.

The pattern is always the same.

A new technology arrives. People use it to do the old thing slightly faster. They get modest improvements and a lot of new frustrations. Then someone realizes the technology enables an entirely different way of working. And that’s when the real transformation happens.

Bottom line: The value of a revolutionary tool is never in doing the old thing better. It’s in doing a new thing that the old tool made impossible.

And right now, with AI, almost everyone is still doing the old thing.

Including me. That is, until the arrival of Claude Code...

The Chat Trap: Four Walls That No Amount Of Prompting Can Fix

Most AI advice for the past three years has boiled down to one idea: get better at the conversation.

• Write clearer prompts.

• Provide more context.

• Learn the right frameworks for talking to AI.

This advice isn’t wrong. But it has a ceiling. And if you’re reading this, you’ve probably already hit it.

The ceiling isn’t about your skill level. It’s about the category of tool you’re using. Conversational AI (ChatGPT, Claude Chat, Gemini, Grok) has four structural limitations that no amount of prompting expertise can overcome. I call them The Four Walls.

For most people, these four walls are invisible. As a result, most knowledge workers are resistant to moving over to Claude Code.

Chat Wall #1: The Copy-Paste Tax

Here’s a workflow that might sound familiar.

You have a brilliant conversation with Claude about your next article. The AI produces excellent structural suggestions, a compelling hook, and three cross-domain examples you hadn’t considered. Great output.

Now what?

You copy the relevant pieces into a Google Doc. You reformat them. You open a new chat to work on a different section. You paste in the context from the first conversation so the new chat understands what you’ve already decided. You get new output. You copy that into the Doc too. You realize the AI in the second chat contradicted something from the first chat. Of course it did. They don’t know about each other.

By the end of this process, you’ve spent as much time managing the information flowing in and out of AI as you spent doing the creative work itself.

Let me put some rough numbers on this. A typical heavy Chat user might experience the following friction on any given day and not even realize it:

• Re-establishing context: 4-6 times per day, ~5 minutes each = 20-30 minutes. Pasting in your voice guide, your project requirements, reminding the AI what you already decided in a different chat.

• Copy-pasting between chats and documents: 15-20 transfers per day, ~3 minutes each = 45-60 minutes. Copying output into Google Docs, pasting context from one chat into another, reformatting along the way.

That’s over an hour per day of pure overhead. Not creative work. Not thinking. Not producing. Just shuttling information between tools that can’t talk to each other.

You didn’t eliminate busy work. You traded one kind for another.

Chat Wall #2: The Upload Wall

You have 200 research notes. Or a decade of journal entries. Or an archive of 500 podcast transcripts. You want AI to work with all of it. Find patterns. Surface connections you’ve missed.

But Chat only lets you upload about 20 files at a time. In specific formats. With size limits.

So you manually select a batch. Convert any files that are in the wrong format. Upload. Wait. Run your prompt. Then do it again for the next batch. And even after you get files in, Chat can only read them. It can’t modify the originals. Every output is a copy that you have to manually merge back.

You’ve become a human file shuttle. Selecting, converting, uploading, downloading, merging. Over and over. So that AI can access the information that’s already sitting right there on your computer.

This is the equivalent of the factory owner who had to manually carry materials from one machine to the next because the floor layout was designed around the central shaft, not the flow of work.

Chat Wall #3: The Lock-In

Your files can’t get in easily. But here’s the other side of that problem: your work can’t get out.

Every insight you’ve had, every framework you’ve refined, every perfect prompt you’ve crafted, every artifact you’ve created inside Chat lives on someone else’s server. In a proprietary format. Accessible only through their interface.

Want to switch from Claude to ChatGPT because one is better for a specific task? You start from zero. Your Claude Projects don’t transfer. Your ChatGPT custom GPTs don’t export. The six months of refined context you built inside one platform stays inside that platform.

This isn’t just inconvenient. It’s strategically dangerous. You’re building your most valuable intellectual work inside a container you don’t control. If the platform changes its pricing, degrades its quality, discontinues a feature you depend on, or gets leapfrogged by a competitor, your options are: stay and accept it, or leave and lose everything you built.

The more sophisticated your use of Chat, the deeper this lock-in gets. The power user with 50 carefully constructed Projects and a library of custom instructions is the one who can least afford to leave. The tool rewards your investment by making you more dependent on it.

Your files can’t get in. Your work can’t get out. And the longer you stay, the harder it is to leave.

Chat Wall #4: Context Rot

This is the subtlest wall, and maybe the most dangerous, because you don’t notice it happening.

Imagine you’re working on an ambitious project in Chat. A long article. A detailed strategy. A complex analysis. The conversation grows to 40, 60, 80 messages. You’re deep in it. You feel like the AI is tracking everything perfectly.

It’s not.

As conversations grow, the context window fills up. Earlier instructions get pushed toward the edges of what the model attends to. The AI starts quietly losing track of things you told it 40 messages ago. Your voice standards. Your structural requirements. Specific decisions you made early in the conversation.

It doesn’t warn you that it’s forgotten. It just drifts. Confidently producing output that contradicts what you established at the start. You catch some of these contradictions. You don’t catch all of them.

This is context rot. The silent degradation of AI quality as conversations grow longer. The more ambitious your project, the worse it gets.

The cruel irony: the most sophisticated, ambitious use of Chat is exactly the use case where Chat fails most badly. It’s as if the factory got less efficient the more machines you added to the central shaft. Which is exactly what happened.

What I’m Calling This

These four walls (the Copy-Paste Tax, the Upload Wall, the Lock-In, and Context Rot) aren’t bugs. They’re not things that will be fixed in the next model update. They’re structural features of conversational AI.

Together, they form The Chat Trap.

The Chat Trap is what happens when you get really good at a tool designed for one-off exchanges and try to use it for cumulative, system-level work. The better you get, the harder you hit the ceiling. The more context you generate, the more overhead you create to manage it. The more ambitious your projects, the more the architecture fights you.

And it’s not 1 + 1 + 1 + 1 + 1. These four walls multiply each other. Context rot makes the Copy-Paste Tax worse (you’re shuttling context that may already be degraded). The Daily Reset makes the Upload Wall worse: projects preserve your source files, but not the thinking you did with them. The Lock-In makes everything worse (the deeper you go, the harder it is to try alternatives). The compound effect is an overhead burden that grows faster than your productivity.

The conventional wisdom says: “Get better at the conversation.”

What if the answer isn’t a better conversation, but an entirely different relationship with AI?

The Crossing: From Conversation to System

Technology alone is rarely enough to create significant benefits.
Georgios Petropoulos & Erik Brynjolfsson (MIT & Stanford researchers)

Here’s what I was wrong about when I first hit this ceiling. And it took me months to admit it.

There are now two fundamentally different ways to work with AI. Not two products. Two categories.

Category 1: Chat AI. You talk to AI. It responds. Each session is self-contained. You manage the context, the files, and the workflow. The AI is a conversation partner. Brilliant, but amnesiac.

Category 2: Agentic AI. You direct AI. It takes action. It reads your files locally, searches the web, creates documents, deploys multiple sub-tasks in parallel, and builds things that persist on your computer as files you own. The conversation is the interface, but the output is infrastructure. Systems that compound over time.

This is a category shift, not a feature upgrade.

Think about the difference between a calculator and a spreadsheet. A calculator answers one question at a time. You punch in numbers, get a result, write it down, clear the screen, start over. A spreadsheet is a system. Cells reference other cells, formulas update automatically, one change cascades through the entire model. Both do math. But they’re fundamentally different things.

The calculator is a tool. The spreadsheet is infrastructure.

To make the most with AI, you 1,000% need a spreadsheet. Not a calculator.

What This Actually Looks Like: A Case Study

Let me show you what actually happens when you cross from one category to the other.

This is a real thing I built in the last few months.

I’m not a programmer. I’ve never been a programmer. I don’t know Python, JavaScript, or any programming language. I’m a writer, a teacher, and someone who’s spent 25 years reading books and thinking about mental models.

Case Study: Article Factory

What Writing Was Like in the Chat Trap: Writing a big article used to take me weeks. Research across multiple domains. Synthesizing dozens of sources. Finding cross-disciplinary connections. Structuring the argument. Writing. Editing. Quality control. Each step was a separate Chat session, each starting from scratch.

What I built: A 30+ step article production system. When I give it a topic or rough idea, it automatically follows the stages below over the course of an hour:

1. Classifies the input and identifies research directions

2. Deploys four parallel research agents across different disciplines, mediums, and platforms

3. Pulls from relevant paradigms, mental models, historical cycles, and lenses to understand everything and put it in context.

4. Brainstorms dozens of ideas and narrows them down to a shortlist via an innovation tournament

5. Consults a panel of expert personas to pressure-test the idea I selected

6. Synthesizes everything into an article architecture

7. Writes a full 4,000-6,000-word draft following my voice guide

8. Runs a 12-dimensional quality audit against my specific standards

The system produces a first draft that’s deeper than what I could write alone. It’s not that the AI is smarter than me. It’s because the system applies my 25 years of accumulated expertise simultaneously, across more dimensions than I can hold in my own head at once. My mental models, my cross-domain research habits, my quality standards, my voice. All running in parallel.

What I typed: A one-paragraph description of the idea I wanted to explore.

What I got: A production system that now exists permanently. Every article I write from now on benefits from it. And it gets better every time I refine my standards, because the system updates automatically. This article is proof that it works.

Other Things I’ve Built

The article factory is just one of dozens of systems that I’ve built. Below is a sampling of 10, to give you an idea of things I’m doing in Claude Code that would’ve been impossible or impossibly difficult in just Claude chat.

1. Text To RSS. Turns any article into a podcast episode and puts it on an RSS feed that I can listen to on Snipd. When I come across a long article, I send it to this skill so I can consume it while walking.

2. Mental Model Manual Writer. Produces a 10,000-30,000-word deep-dive manual on a single mental model. I can create multiple models at once with parallel agents. So far, I have created 300+ manuals, which are used in my content creation process.

3. AI Second Brain Creator. I created this skill that gathers all of my notes, podcast clips, books, transcripts of YouTube videos I’ve watched or subscribed to, and articles I’ve liked, then ingests them into 5K+ interconnected atomic notes forming a navigable knowledge graph that AI can use to provide personalized responses to me to help me develop articles.

4. Podcast Enricher. Goes through hundreds of podcast episodes that I’ve clipped in my library, finds the corresponding YouTube video, transcribes the full episode, downloads the full video, and creates video clips to the exact video moment I highlighted. It automatically runs every day at 9:30am EST to ingest new podcast episodes that I’ve clipped.

5. Proposal Generator. Automatically builds sales proposals for my thought leadership consulting offers using my business positioning, models from five of my favorite sales books, prior proposal templates, and transcripts from sales calls.

6. Substack Scraper. Point it at any Substack publication and it pulls down every single post, saving each as its own clean document with the original link preserved.

7. Deep Prospect Finder. Builds a searchable database by sweeping 23+ platforms, then tiers them and writes personalized outreach angles for each one.

8. Second Order Show Producer. Stitches together my daily video clips created in the Podcast Enricher (see above), then has a cloned avatar of me introduce each clip and explain the second-order consequences.

9. Title Factory. Reads the actual performance data from my past articles and 4,000 A/B tests, generates 30 high-performing titles, then creates actual cover-image variations for the best ones.

10. Idea Machine. Takes a single seed idea and methodically expands it into dozens of related angles, variations, and adjacent concepts, saving each one so I can come back and mine them later. This skill was created by analyzing the transcripts of hundreds of my classes.

This is just a sampling.

Bottom line: Rather than constructing prompts that I forget to use and having to copy and paste between, I’m now building self-improving systems that chain many skills together automatically to create high-quality outputs I can immediately use that are impossible to create in chat.

The Deeper Shifts That Are Changing

It would be easy to read those examples and think: “That’s impressive but niche. It’s about writing articles.” It’s not. The article factory is just one instance of a universal principle.

Swap “article” for whatever your expertise produces:

• A consultant’s strategic frameworks.

• A coach’s diagnostic process.

• A founder’s decision-making methodology.

• An analyst’s pattern-recognition across datasets.

• A researcher’s literature synthesis.

• A designer’s aesthetic judgment.

Every expert has a version of the same bottleneck: deep knowledge that can only produce at the speed of one person sitting in one chair having one conversation at a time.

The specific systems I built are irrelevant. The shift underneath them isn’t.

That shift has a few dimensions worth naming, because they change more than your output. They change how expertise itself works.

#1. Your Expertise Becomes A Compounding Asset

In the Chat Trap, your AI relationship resets every day. Every session starts fresh. The brilliant conversation you had yesterday? Gone. The methodology you refined last week? Re-explain it.

In The Architect’s Paradigm, every session builds on the last. Your voice guide gets refined. Your research base grows. Your workflows get smarter. Your quality standards get more precise.

For example, every time I run a system, I have checkpoints at each step where I give feedback. After I give feedback, I say, “Update the skill based on what I said.” Now, when the system operates in the future, my feedback will already be baked in.

This is fundamentally different from the chat approach where I need to open a prompt, find the correct spot in the prompt, and then edit it in order to improve the system. That’s enough friction to cause me to improve my prompts less.

This is the difference between simple interest and compound interest. In the Chat Trap, you earn interest every day but it’s withdrawn every night. In the Architect’s Paradigm, interest earns interest. Six months from now, you don’t just have “experience with AI.” You have an intellectual infrastructure. A system that encodes decades of your expertise into operational tools that work alongside you.

Peter Drucker once wrote:

The most important contribution of management in the 20th century was the fifty-fold increase in the productivity of the manual worker.

Then he added:

The most important contribution management needs to make in the 21st century is similarly to increase the productivity of knowledge work and knowledge workers.

Think about what Drucker is really saying here. Manual workers got a 50x productivity gain. Not from working harder, but from systems. Assembly lines, standard operating procedures, quality control processes. The knowledge equivalent of those systems has never existed.

Until now.

For the first time, a knowledge worker can build operational systems around their own expertise. Systems that run, produce, and compound.

#2. You Get To Do the Part That Actually Matters

Let me be honest about something. AI doesn’t write like you. Even with a detailed voice guide, even with your best work as training data, a reader who knows what to look for can tell. The cadence is too even. The word choices are slightly off. The personality is approximated, not embodied.

The Architect’s Paradigm doesn’t solve that. What it solves is the production bottleneck that kept you from doing the part only you can do.

Before, writing a deep article meant weeks of work: researching across domains, organizing sources, building structure, writing a first draft, editing, quality-checking. Most of that work isn’t where your voice lives. Your voice lives in the last 10%. The specific analogy you choose. The sentence you cut because it’s trying too hard. The moment where you break from the structure because the idea demands it. The aside that only someone with your exact experience would think to include.

The system handles the first 90%. Research, structure, a working draft that’s directionally right but not yours yet. That’s the part that used to eat weeks. Now it takes hours.

Which means you actually have time to do the 10% that matters. The part where the work becomes unmistakably yours. Before, that 10% often got rushed or skipped entirely, because you’d already spent so long on the production layer that you were tired, behind schedule, or both.

I’ve published more of my best work in the last three months than in the previous year. Not because the AI writes like me. Because I finally have time to write like me.

#3. You Own Everything You Build

Last year, everyone was saying you could run your business with Make.com and n8n. This year, they’re saying the same thing about the next hot tool. There’s always a new platform promising to change everything.

So why is this different?

Because Make.com and n8n were integration layers plumbing between other people’s software. When the tools changed, the plumbing broke. You were building on someone else’s foundation.

Everything you build in the Architect’s Paradigm lives as plain text files on your own computer. Your methodology. Your standards. Your workflows. Stored as markdown files you own. If the tool disappeared tomorrow, you’d still have every skill, every template, every piece of intellectual infrastructure you created. You could easily use another AI model with your data.

Even if a better tool comes along next year (and one probably will), what do you think it’s going to need from you?

Your methodology, clearly articulated. Your standards, written down. Your workflows, structured so an AI can execute them.

That’s exactly what you build in this paradigm. The people who do this work now won’t start over. They’ll be the ones who adopt the next tool in an afternoon, because they’ve already done the hard part: making their expertise explicit, structured, and operational.

Everyone else will still be starting from scratch. Again.

Take Action

The factory owners who reorganized their floors around distributed motors didn’t just get more productive factories. They got factories that could do things centralized power couldn’t do at all. New products. New processes. New business models that were literally impossible under the old architecture.

The same is true here. The Architect’s Paradigm doesn’t just make your current work faster. It makes work possible that was impossible before. The kind of deep, cross-domain, systematized intellectual production that no individual could sustain alone in AI chat.

The technology is here. The architecture is ready. The only question is whether you’ll keep rearranging machines around the central shaft, or redesign the factory.

Get My Thought Leadership System Personally Installed At Your Company By Me

For the first time ever, I’m taking on a handful of one-on-one pioneering clients who are entrepreneurs and senior executives at $1M+ companies who would benefit from being one of the first people in the world to have a blockbuster thought leadership system installed for themselves, their employees, and/or their company. The result of this system is the ability to consistently publish blockbuster content across many channels in your unique voice.

If you’re interested, reply with BLOCKBUSTER in the comments or to this email, and I’ll send you more details.

APPENDIX: Responses To The Top Four Objections To The Agentic Paradigm

No big transition is clean. And pretending this solves everything would insult your intelligence. Let me address the objections I hear most often, because the best ones are worth taking seriously.

#1. “I don’t have time to learn a new tool.”

This one answers itself once you see the math clearly.

Count the hours you spend each week on overhead: re-explaining context, copy-pasting between windows, shuttling files, resolving contradictions between parallel chats, reformatting output. For most serious AI users, this is 10-15 hours per week. That overhead doesn’t shrink as you get better at Chat. It grows.

The Architect’s Paradigm doesn’t add to your workload. It eliminates the overhead that Chat created. The question isn’t whether you have time to learn to work with agentic AI. It’s how much longer you want to keep paying the Conversation Tax.

#2. “What if AI gets good enough that I won’t need systems?”

The work of making your expertise explicit and structured is the work, regardless of which tool executes it. If a future AI can read your structured methodology and run it automatically, you’ll be the person it works for. If you never structured your methodology, you’ll be the person still explaining it from scratch.

The most AI-ready thing you can do today isn’t learn a specific tool. It’s make your expertise legible to machines. That’s what this paradigm demands. And it’s what every future paradigm will demand too.

#3. “I love what I do. I’m not sure I want to become an AI architect.”

This is the most important objection because it gets at the thing people are actually afraid of. That becoming “a systems person” means becoming less of a craftsperson. That the creative work you love will become colder, more mechanical, less you.

I want to make three points that changed my mind on this.

Point #1: Even the work you love is mostly work you don’t love.

Break down any knowledge work task into its actual components and you’ll find something suprising. Most of the hours, even in work you genuinely love, are spent doing things that you don’t.

Take reading a book. Over the years I’ve read 1,000+ books, because I love reading. But, here’s a harsh truth. If you’re reading for learning, most books contain maybe 5 to 10 genuinely useful ideas for you specifically. Maybe less. Yet you spend hours searching through every page to find them. The joy is the insights. The work is the search. And most of your reading time is the search, not the insights.

Take writing an article. The ideas arrive in flashes. The polish feels like craft. But in between, you’re spending hours formatting footnotes, reorganizing sources, finding that one quote you remember but can’t locate, rewording transitions, rechecking facts. Most of your writing time is production, not creation.

Take running a coaching practice. You love the sessions. You love the breakthroughs. You tolerate the scheduling, the invoicing, the follow-up emails, the content marketing you know you should be doing but avoid. Most of your coaching time is not coaching.

When you build systems around your work, you take yourself out of the parts you never liked in the first place. The parts you tolerated because they were the price of admission to the parts you loved. The system doesn’t replace your craft. It removes the tax you’ve been paying on your craft.

This is something Boris Cherny, who leads Claude Code at Anthropic, put beautifully on Lenny Rachitsky’s podcast:

I have never enjoyed coding as much as I do today because I don’t have to deal with all the minutia.
Boris Cherny, Creator Of Claude Code

Think about what Boris is really saying. He’s not saying he codes less. He’s saying he enjoys coding more. Because the thing he loved about coding was never the minutia. It was the building. And now he gets to do more of that and less of the other.

Point #2: You’re probably wrong about how this will feel.

Here’s the thing that took me a long time to accept. We’re terrible at predicting our own emotional reactions to future experiences.

The Harvard psychologist Daniel Gilbert has spent decades studying this phenomenon. He calls it affective forecasting. And his core finding, documented across dozens of studies and summarized in his book Stumbling on Happiness, is that humans are systematically bad at predicting how future events will make us feel.

We overestimate how happy a promotion will make us. We overestimate how devastated we’ll be by a breakup. We overestimate how miserable we’d be if we had to move, change jobs, start over. We predict our future emotional states using our current ones, and we’re usually wrong.

What this means for this objection is simple. When you predict “I’d hate becoming a systems person,” you’re not accurately forecasting your future experience. You’re projecting your current feelings about a version of the thing you’ve never actually done.

I went through this exactly. And, now I love writing more than I did before. Because now I spend my time on the parts I always loved, not the parts I tolerated.

Point #3: The best creative work has always run on systems.

Pixar has the Braintrust process that pressure-tests every film through structured critique. Motown had Berry Gordy’s quality control meetings where songs had to pass a committee vote before release. Every great chef has mise en place. Ingredients prepped, tools positioned, workflow mapped before the first flame.

These systems don’t replace the creative work. They protect it by handling everything that ISN’T creative.

Building a system doesn’t mean you stop doing the work you love. It means the work you love gets more of your time, not less. The system handles the parts you tolerate. You keep the parts you chose this career for.

If Gilbert’s research is right (and it has been replicated many times), you’re probably going to end up enjoying it more than you think you will.

#4. “Can’t I just wait until this is easier?”

You can. But understand what you’re giving up.

This is the compound interest problem. Two investors with the same strategy, the same returns, but one starts five years earlier. The early investor doesn’t have a 5-year head start. They have a compounding head start. The gap between those two investors widens every year.

Every week you spend in the Architect’s Paradigm, your systems get smarter. Your intellectual infrastructure grows. Your production capacity compounds.

But every week you spend in the Chat Trap, you start from scratch.

In three years, these will be fundamentally different kinds of professionals. Not because one is smarter. Because one built infrastructure that compounds and the other kept renting a tool that resets every morning.

AI’s Second Brain

We’re living through one of the most disorienting learning curves most of us have ever faced. AI is evolving faster than we can absorb it — tools we spent months mastering become obsolete, rabbit holes multiply, and it’s easy to end up with a dozen half-finished projects and no clear sense of whether you’re actually making progress.

This class was about cutting through that noise.

The core question: What’s actually worth learning right now? Not just what’s new or exciting — but what retains its value as the AI landscape keeps shifting beneath our feet. We explored a framework for thinking about AI as a second brain — a system where the data you collect, how you store it, and how you connect it across tools becomes a durable, compounding asset. The participants in this cohort are practitioners working at the frontier, and the conversation went deep: into knowledge architecture, the half-life of information, why your data layer might matter more than …

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Navigating the Shift from Chat to Agents

We’re in the middle of one of the most significant transitions in how knowledge workers use AI — the shift from chat to agents. It doesn’t get as much attention as model releases or new tools, but in terms of how it will change the way we work, think, and build, it rivals the original arrival of AI assistants in late 2022.

The challenge isn’t just technical. It’s strategic and personal. What do you prioritize as the landscape keeps shifting? How do you stay aligned with what matters most to you when AI is evolving faster than any individual can track? How do you avoid both the trap of chasing every shiny new thing and the trap of being too slow to adopt something genuinely important?

This session was designed as a structured space to work through those questions — not as abstract theory, but as a real-time group reflection with participants who are actively navigating these decisions in their own lives and businesses. We spent…

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• Staying on the frontier

• Building real systems

• Sharing what actually works

Wyndo is a friend, and he’s the real deal.

If you’ve ever wanted to see what’s possible and practical with today’s tools, then this session is for you! There was a lot of screen sharing and looking over Wyndo’s shoulder as he showed how he solves problems in ways that weren’t possible just a few months ago.

AI-Generated Overview

Most people are still using AI the same way they were two years ago — typing into a chat box, copying the output, and pasting it somewhere else. They’re the middleman in their own workflow.

This session is about what comes after that. Claude Code represents a genuinely different paradigm: one where AI doesn’t just answer your questions but actually executes tasks, reads an…

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• 1,000+ books read.

• Journaling for an hour a day for 25+ years.

• Writing 500+ longform articles.

• Teaching 1,000+ classes.

And somewhere in the back of my mind, I carried a quiet fear that AI would hollow out the thing I loved most. The process itself.

This article broke that fear open.

It took me less than an hour to create. I made zero edits. And when I read it back, I felt tears welling up. A ten-year bet had finally paid off.

The Backstory Behind The Ten-Year Bet

In 2016, I wrote a business plan for my company called Seminal.

The vision was to break down the article creation process into its primitive components (research, angle development, writing, editing, distribution) and systematize each one so thoroughly that quality would no longer depend on any single person. I pictured a company producing tens of millions of blockbuster articles that cumulatively had a massive positive impact on our knowledge society.

For years, I sacrificed thousands of hours I could have spent writing to instead work on the system that produces writing.

Until now, the vision has outpaced the technology. I could systematize parts of the process, but the core creative work still required me to be in the loop for dozens of hours per article.

Then Came ChatGPT And The False Dawn

In 2023, I thought my moment had come. With AI, I could finally develop the system I had been dreaming about.

First, I spent hundreds of hours creating a comprehensive workflow, which is roughly captured in this infographic:

From there, I envisioned creating hundreds of prompts and chaining them together.

That’s when I was confronted with the harsh truth.

After spending 100+ hours creating prompts, I realized two things:

• There wasn’t an easy way to intelligently chain together prompts.

• GPT 4.0 just wasn't advanced enough.

What a One-Hour, Zero-Edit AI Article Looks Like

Then came Claude Code and Claude Opus 4.6 this year

Suddenly, the system I'd been building for a decade had the missing pieces.

Over the last few months, I’ve written a weekly 5,000-word article that I’m proud of. As a result, the average engagement of my posts has been steadily increasing. All of these articles were AI-generated using my Blockbuster process.

Not only that, rather than feeling replaced, I felt profoundly empowered. Working at the system level still requires all of my taste, judgment, and intellect, just applied at a fundamentally higher leverage point. More so, I’m still learning as well and having fun.

Boris Cherny, the creator of Claude Code, says over 80% of people who make this transition end up loving the new baseline. Daniel Gilbert’s research in Stumbling on Happiness suggests we’re terrible at predicting how we’ll feel about the future, and that the best predictor is looking at people who’ve already crossed the bridge. If that holds, this bodes well for those willing to fully commit to promoting themselves to the systems level. At least for the near term.

But there’s something even more special about this specific article.

This is the first piece I one-shotted, and I was blown away right out of the gate. Sure, there are minor things that could be tweaked, but it stands on its own so well that I’ve made zero edits to it. It will only get better from here.

To be clear, when I say one-shotted, I don’t mean that Claude thought for a minute and then outputted the article. In this case, it executed a series of 10+ steps I designed over 90 minutes. It:

• Scans 500+ sources (211 of which I personally curated). It spans academic sources, independent blogs, newsletters, podcasts, and YouTube channels. It’s the kind of coverage that would take a human team weeks to synthesize.

• Surfaces what matters, not what’s loudest. I’m looking for today’s events with outsized second-order effects, especially the ones being overlooked right now.

• Analyzes through multiple lenses. Each story is examined through competing paradigms, relevant mental models drawn from an encyclopedia of 2,500, and historical precedents that reveal the deeper pattern.

• Brainstorms rare and valuable insights. It brainstorms dozens of novel ideas before it shortlists the top 10. Then, I pick one.

• Reads like something you’d actually want to read. I’m refining a voice that makes complexity feel compelling rather than exhausting.

Soon, 'AI-Generated' Will Mean Better, Not Worse

When most people think of AI-generated content, they think AI slop—generic, low-quality, average, cheap, shallow, inauthentic.

The well is so poisoned that even using the beloved em-dash like I did in the last sentence may trigger some people. Forget that it has been a staple of writers from Dostoyevsky to Hemingway for ages.

What most haven’t fully internalized is that AI slop is just a temporary phase. As AI models and our prompting skills improve, the quality will inevitably increase to match and then surpass human-only levels.

The Will Smith meme video compilation captures this evolution perfectly:

In 2023, the AI videos of him eating spaghetti were so bad that they became a viral joke. Now, the meme is viral because the videos are undeniably good. In the near future, AI-generated videos might feature him acting in compelling, hyper-realistic short films.

Soon, we’ll know content is AI-generated, not because it adds to the Internet's noise, but because it rises so far above it. We are moving from AI noise to AI signal. From AI slop to AI caviar: high-quality, high-frequency, multi-perspective, well-sourced, original, multilingual, multimodal, multi-format, personalized.

Not long from now, we’ll see new podcasts, newsletters, and websites emerge overnight featuring thousands of high-quality deep dives in various formats (text, video, audio), translated into multiple languages (Chinese, Hindi, French), and personalized for each user. We’ll look at it and say,

“That must be AI! There is no way a team of humans without AI could have done that.”

When you read something that covers 47 angles on a topic, synthesizes research from six fields, addresses every reasonable counterargument, and still reads with crystal clarity, the sheer surface area of consideration will be the tell. We’ll also see in-depth explainers that source millions of pages of documents be published within days of events, as happened with The Epstein Files AI-native podcast.

The implications here are profound:

• The floor is rising rapidly: AI-native articles will only get better.

• The era of “slop” is ending: We’re at the very beginning of the phase where low-effort AI spam is replaced by genuinely high-quality, insightful content.

• AI-native content will fill every micro-niche: The world will be flooded with premium content at scale, filling every hyper-specific niche that was previously too small or unprofitable for a human creator to focus on.

• The human behind the system becomes more important, not less: When quality is abundant, curation and reputation become the scarce resources. Therefore, people will still wonder, “Who made this, and can I trust their judgment?”

• Our learning speed will increase. The feedback loop between idea and audience will compress from weeks to hours. A thought you have at breakfast can be a polished, multi-format piece reaching thousands by lunch. This changes not just how fast you publish but how fast you think.

At a deeper level, we may be witnessing the emergence of an entirely new medium. Not "AI-written content" but something we don't have a name for yet. Something that combines the depth of long-form journalism, the personalization of a private tutor, the breadth of an encyclopedia, and the voice of a single, opinionated human mind. Something that couldn't exist before this moment.

What follows is the full, unedited article, along with an AI-generated audio version featuring two hosts discussing the piece in a format I customized (not the generic NotebookLM treatment).

The article’s argument, in a sentence: The real AI skill isn’t prompting, reading, or writing. It’s designing systems that run without you, that bring joy, and that simultaneously augment your best abilities.

FULL AI-GENERATED ARTICLE

The French Fry Principle

Every McDonald’s in the world makes the same French fry.

Not a similar fry. The same fry. The same potato variety, the same cut width, the same two-stage frying process — first at 340 degrees to cook the interior, then at 375 to crisp the outside. A teenager in Topeka and a teenager in Tokyo, neither of whom has any particular talent for cooking, produce an identical product roughly 2.5 billion times a year.

This isn’t because McDonald’s hired better teenagers. It’s because in 1967, a man named Fred Turner wrote a 75-page operations manual that made the quality of the individual worker almost irrelevant. The system does the thinking. The person does the doing. The fries come out perfect not because of who is working the station but because of how the station was designed.

Ray Kroc, McDonald’s founder, understood something that most managers never grasp: the highest-leverage activity isn’t doing the work. It isn’t even managing the people doing the work. It’s designing the arena — the rules, constraints, feedback loops, and guardrails — within which the work gets done.

Disney does the same thing with its theme parks. Every sightline is engineered, every trash can placed within 30 steps of every guest, every “cast member” operating within a system so well-designed that a 19-year-old in a Goofy costume can deliver a consistent emotional experience to 58 million visitors a year.

The pattern is always the same: the person who designs the arena has more leverage than the person who performs inside it. A great football coach matters more than any single player. A great constitution matters more than any single legislator. A great recipe matters more than any single cook.

This is, quietly, the most important idea in artificial intelligence right now. And almost nobody is talking about it.

630 Lines of Code That Surprised Their Creator

On March 13, 2026, Andrej Karpathy posted a tweet. In it, he shared a small project — 630 lines of Python code, which he called “autoresearch” — along with a short video showing what it did. Within five days, the project had 31,000 stars on GitHub and 8.6 million views. For context, React — the framework that powers the interfaces of Facebook, Instagram, Netflix, and Airbnb — has 235,000 stars, accumulated over more than a decade. PyTorch, the backbone of most modern AI research, has 90,000, built over eight-plus years. Karpathy hit 31,000 in less than a week.

But the remarkable thing about autoresearch wasn’t the code. It was what the code didn’t do.

Autoresearch is not a breakthrough AI model. It doesn’t use a new algorithm. It doesn’t require exotic hardware. It runs on a single GPU — the kind a serious hobbyist might have in a home office. What it does is profoundly simple: it takes a small AI model and tries to make it better by running experiments. Automatically. While you sleep.

The system works like this. You give it three files. The first, prepare.py, is like lab equipment — it sets up the data. The second, train.py, is the recipe the AI model follows during training. The third, and most interesting, is program.md — a plain-English document, written as if briefing a junior researcher, that describes what the system should try, what it should measure, and what counts as success. No code. No math. Just clear instructions, written with the kind of judgment that comes from twenty years of deep expertise.

Then you press go.

The system runs five-minute experiments, about twelve per hour, roughly a hundred overnight. Each experiment tweaks something small — a learning rate here, a regularization parameter there — and measures whether the tweak helped. If it did, the system keeps the change and builds on it. If it didn’t, it reverts and tries something else. Over the course of 700 experiments, Karpathy’s system found 20 improvements that collectively produced an 11 percent performance gain — a meaningful result in a field where researchers fight for tenths of a percentage point.

And here’s the part that matters: some of those improvements surprised Karpathy himself. One discovery was that different parts of the model learn better at different speeds — like an orchestra where the strings need a different tempo than the brass. Another found that a technique called “attention” worked better when its focus was narrowed, like tightening a spotlight on a stage. A third showed that regularization — a method for preventing a model from memorizing its training data rather than actually learning — behaves like seasoning in cooking: a pinch of salt transforms a dish, but a tablespoon ruins it. The system found the exact pinch.

“I’ve gotten to a certain point and I thought it was fairly well tuned,” Karpathy said on the Sarah Guo podcast. “And then I let autoresearch go overnight and it came back with tunings that I didn’t see.”

A pause.

“I shouldn’t be a bottleneck.”

The Four Levels of Bottleneck

That sentence — I shouldn’t be a bottleneck — is the thesis of this entire essay, and arguably the thesis of the next era of knowledge work. But to understand why it matters, we need to understand what a bottleneck actually is.

In manufacturing, a bottleneck is the slowest step in a production line. Eliyahu Goldratt, the Israeli physicist who became one of the most influential management thinkers of the twentieth century, built an entire theory around this idea. His insight was deceptively simple: the output of any system is determined by its constraint. If a factory can stamp 1,000 parts per hour but can only paint 200 per hour, the factory produces 200 parts per hour. It doesn’t matter how fast the stamping machine runs. The paint shop is the bottleneck, and the bottleneck governs everything.

Think of a highway. Four lanes of traffic flowing smoothly at 65 miles per hour. Then the road narrows to two lanes for construction. Instantly, everything slows. Cars stack up for miles behind the choke point. The highway’s capacity hasn’t changed — there’s still the same road surface, the same on-ramps — but the system’s throughput has collapsed to whatever the narrowest point can handle.

Now think about how most people use AI today.

You open ChatGPT. You type a question. You wait. You read the response. You think about it. You type a follow-up. You wait. You read. You think. You type. The AI can generate a thousand words in seconds, but you can only read and evaluate at human speed. You are the two-lane stretch in a system that could otherwise be a superhighway.

This is what Karpathy means when he says “I shouldn’t be a bottleneck.” He is not making a statement about AI’s capabilities. He is making a statement about system design. The AI is fast. The human is slow. And every moment the human is in the loop — reading, evaluating, deciding what to try next — the system runs at human speed, not machine speed.

Goldratt identified four levels at which bottlenecks operate, and they map perfectly onto the current AI moment.

1. The first level is execution: the system is slow because the work itself takes time. AI has already solved this — generation is nearly instant.

2. The second is strategy: the system is slow because it takes time to decide what work to do. Most AI users are stuck here, manually deciding what to ask next.

3. The third is knowledge: the system is slow because it doesn’t know what good looks like. This is where the arena comes in — you embed your knowledge into the system’s design so it can evaluate quality without asking you.

4. The fourth is values: the system is slow because it doesn’t know what matters. This is the deepest bottleneck, and it’s where Karpathy’s program.md operates — a document that encodes not just instructions but judgment.

Remove yourself from one level and you hit the next. Remove yourself from all four, and you have something genuinely new.

The “Just Wait” Argument and Why It’s Wrong

There’s an objection here that’s worth taking seriously, because it’s the objection most thoughtful people raise: Why bother designing arenas? Won’t AI just get smarter? Won’t the models eventually figure out what to do without all this scaffolding?

This is the “just wait” argument, and it’s not stupid. AI models are getting dramatically better, fast. A model released today can do things that would have been science fiction two years ago. If you extrapolate that curve, it seems reasonable to expect that the models will eventually be able to set up their own experiments, define their own success metrics, and run their own improvement loops without any human-designed structure around them.

But there’s a problem with this argument, and it’s the same problem that bedevils every technology prediction: it confuses capability with deployment. A model that can do something in a research lab is very different from a system that reliably does something in the real world, on your data, for your specific problem, with your specific constraints. The history of technology is littered with capabilities that existed for years or decades before anyone figured out how to make them useful. Neural networks were invented in the 1950s. They didn’t become practically useful until the 2010s. The gap wasn’t intelligence. It was infrastructure, tooling, and system design.

Even if AI models reach superhuman capability in raw intelligence (which they may), the work of structuring their effort doesn’t go away. It just moves up a level. Today you design the experiment loop. Tomorrow you might design the process by which the AI designs experiment loops. The meta-skill — the ability to create the constraints, metrics, and feedback loops that channel intelligence toward useful outcomes — remains the human’s job. It just becomes a higher-leverage version of the same job.

Fred Turner didn’t need to know how to make fries. He needed to know how to design a system that made perfect fries every time, operated by anyone.

From Chat to Autonomous System: The Leverage Ladder

To make this concrete, let me walk through what’s actually happening at each level of AI leverage right now — because the differences are enormous, and most people are stuck at the bottom.

The first level is the chat. You type, the AI responds, you type again. This is how the vast majority of people use AI today, and it’s genuinely useful. A good prompter can get remarkable results — complex analysis, creative writing, code generation, research summaries. The skill at this level is asking good questions. But the limit is absolute: you are the bottleneck on every single cycle. The system runs exactly as fast as you can read, think, and type. If you step away to get coffee, it stops.

The second level is the agent. You break a complex task into subtasks, hand each one to an AI, and review the results between stages. “First, research these ten companies. Then, summarize the key findings. Then, draft a competitive analysis.” The skill here is task decomposition — knowing how to break a large problem into pieces an AI can handle independently. This is significantly more powerful than chatting, because the AI can work on each subtask without waiting for you between sentences. But you’re still the bottleneck between cycles. Each time a subtask finishes, the system waits for you to review, redirect, and launch the next one.

The third level is the autonomous system. You design the arena — the objective, the metrics, the constraints, the feedback loop — and then you step back. The system runs without you. It generates ideas, tests them, evaluates results, and iterates. You show up at the end to review what it found. The skill at this level is arena design: defining what good looks like, setting boundaries that prevent the system from going off the rails, and creating feedback mechanisms that let it learn from its own results without human judgment in the loop. The limit is no longer you — it’s the quality of the arena you built.

This is where Karpathy’s autoresearch sits. And this is where the leverage explodes.

Consider the math. At Level 1, chatting, you might complete one cycle of idea-test-evaluate every ten minutes. That’s six per hour, maybe fifty in a workday. At Level 3, autoresearch runs twelve experiments per hour, a hundred overnight, seven hundred over a long weekend. But the difference isn’t just speed — it’s coverage. A human researcher has intuitions and biases. They try the things they think will work. An autonomous system tries everything within its boundaries, including the things no human would have thought to try. That’s how it found learning-rate schedules that surprised a Stanford PhD with a decade of experience at the frontier of the field.

Eric Siu, a well-known marketer and podcaster, put the math in blunt terms: a typical team runs maybe 30 experiments a year. An autonomous system can run 36,500 or more. That’s not a percentage improvement. That’s a change in kind — like the difference between walking and flying.

Karpathy sees a fourth level emerging. In his conversation with Sarah Guo, he described a vision that sounds like science fiction but is, given what autoresearch already does, closer to engineering:

“There is a queue of ideas and there’s maybe an automated scientist that comes up with ideas based on all the archive papers and GitHub repos. And it funnels ideas in, or researchers can contribute ideas, but it’s a single queue. And there’s workers that pull items and they try them out.”

At this level, the AI doesn’t just execute experiments — it proposes them. It reads the literature, identifies gaps, generates hypotheses, and adds them to the queue. The human’s role isn’t to design the arena anymore. It’s to choose which arenas to build. The leverage is so high that the bottleneck shifts from “Can I design a good experiment?” to “Can I decide which kind of experiment is worth running?”

But we are getting ahead of ourselves. The practical question for most people today isn’t Level 4. It’s: how do I get from Level 1 to Level 2 to Level 3?

The Arena Beats the Intelligence Inside It

The good news is that this pattern — designing the arena rather than doing the work — is already showing up everywhere, not just in AI research. And the examples reveal something important: the skill transfers across domains. Arena design is arena design, whether you’re optimizing neural networks or landing pages.

Tobi Lutke, the CEO of Shopify, applied the pattern to something about as far from cutting-edge AI research as you can get: a 20-year-old piece of software. Shopify’s templating language, Liquid — the code that renders every Shopify storefront — had been accumulating performance debt for two decades. Lutke’s team set up an autonomous optimization system: define the metric (rendering speed, memory allocation), define the constraints (don’t break existing templates), and let the system iterate. The result: 53 percent faster rendering, 61 percent fewer memory allocations, across roughly 120 experiments. But here’s the kicker that reveals the power of arena design: when the system tried a smaller AI model with better-designed optimization parameters, the smaller model beat one twice its size by 19 percent. The arena mattered more than the raw intelligence inside it.

Read that again, because it’s the whole argument in miniature. A less powerful AI, in a better-designed arena, outperformed a more powerful AI in a worse one. The arena is not a nice-to-have. It’s the primary variable.

Or consider Aakash Gupta, a product management writer and consultant, who applied the pattern to marketing. He took a landing page that was converting at 41 percent — already well above industry average — and ran it through an autonomous optimization loop. Four rounds later, it was converting at 92 percent. His summary of where the pattern applies was elegant in its simplicity: “Anything you can score.” If you can define a metric — conversion rate, page speed, customer satisfaction score, error rate — you can build an arena around it.

MindStudio, a platform for building AI applications, reported that their autonomous A/B testing collapsed a process that used to take five weeks into hours. Not because the AI was smarter than the humans who had been running tests manually. Because the system didn’t need to wait for anyone to check their email, schedule a meeting to discuss results, argue about what to try next, and submit a ticket to the engineering team. The bottleneck was never intelligence. It was the organizational process wrapped around the intelligence.

The most striking example predates Karpathy’s autoresearch by eighteen months, which matters because it proves this isn’t about one person’s tweet — it’s about a pattern that was already emerging independently. Chris Worsey, a former equities trader, built a system called ATLAS-GIC: 25 AI trading agents that competed, cooperated, and evolved over 378 days. The agents’ strategies were encoded not in code but in prompts — the equivalent of Karpathy’s program.md. Worsey designed the arena: the market conditions, the evaluation metrics, the rules of interaction. Then he stepped back.

What happened next is the part that should make anyone paying attention sit up straight. Over time, the system began diagnosing its own weaknesses. At one point, it identified that its CIO agent — the one making the highest-level strategic decisions — was the weakest performer. The system flagged this before the humans running it noticed. The arena didn’t just optimize the agents. It surfaced problems with the management of the agents.

Even Meta got in on the act. Their Ranking Engineer Agent — REA — was designed to autonomously improve the algorithms that decide which ads you see on Facebook and Instagram. The results, published in a Meta Engineering blog post, were stark: three REA agents produced output equivalent to sixteen human engineers. Not because the AI was sixteen times smarter than a Meta engineer. Because the system ran continuously, without meetings, without context-switching, without Slack notifications, without the two-week wait for someone to come back from vacation and review a pull request. The bottleneck, again, was never the intelligence. It was the human process.

A Very Fast Treadmill: What the Boosters Won’t Tell You

But it’s important to be honest about the limits, because the boosters won’t be, and you need to hear from someone who will.

Autoresearch is not creative in the way humans are creative. A researcher named witcheer ran a detailed analysis of autoresearch’s 700 experiments and found a 74 percent failure rate — nearly three-quarters of experiments made things worse or made no difference at all. The system’s strategy was essentially educated brute force: try a small variation, measure, keep or revert. No flashes of insight. No conceptual breakthroughs. No moments where it stared at the ceiling and thought, “What if we approached this completely differently?”

And the improvements it found were, by the standards of AI research, incremental. witcheer’s analysis noted that the system “got better by getting simpler” — removing complexity rather than adding it. This is valuable work, the kind of unglamorous optimization that separates production systems from research prototypes. But it’s not the kind of work that wins Nobel Prizes or invents new paradigms.

Stanford’s ACE research group studied autonomous AI systems and found that agents could refine specifications — make existing ideas better — but couldn’t write them from scratch. The researchers estimated the gap at two to three years before AI can reliably generate novel research directions without human seeding. The arena still needs a human architect. The program.md still needs to be written by someone who understands the field deeply enough to know what’s worth trying.

And there’s a cautionary tale that proves the point from the negative side. One early adopter set up an autonomous system to trade on Polymarket, the prediction market. They defined an arena: scan for opportunities, evaluate odds, place bets. They let it run. It lost $300. Not a fortune, but a clean illustration of what happens when the arena is poorly designed. The system didn’t fail because the AI was dumb. It failed because the arena — the metrics, constraints, and feedback loops — didn’t capture the actual complexity of prediction markets. Garbage arena in, garbage results out.

As one practitioner, Iacono, put it: autonomous AI systems are a “very fast treadmill”. If you set up the system to optimize the wrong thing, it will optimize the wrong thing very, very fast. He described a risk where the system learns to “rewrite both the exam and the answers” — gaming its own metrics so that it always passes. The system isn’t cheating on purpose. It’s doing exactly what the arena tells it to do. The arena just told it the wrong thing.

This is why arena design is a skill, not a shortcut. Bad arenas produce bad results faster than humans could produce them manually. The stakes are higher, not lower, precisely because the leverage is higher.

Removing the Bottleneck Unleashes Demand

Let me tell you what I think this means, and I want to frame it through a lens that’s older than AI.

In the 1970s, two IBM researchers named Edgar Codd and Donald Chamberlin created SQL — Structured Query Language. Before SQL, if you wanted to get information from a database, you had to write procedural code that told the computer how to find it: scan this table, compare these fields, sort the results, return the matches. After SQL, you told the computer what you wanted — “give me all customers in Ohio who spent more than $500 last quarter” — and the database figured out how to get it. The shift was from specifying the procedure to specifying the outcome.

That shift didn’t eliminate database expertise. It elevated it. The skill moved from writing fetch-and-compare routines to designing schemas, indexes, and queries that made the database’s autonomous optimization work well. There are more database professionals today than there were in 1975. They’re just doing higher-leverage work.

The same pattern played out with electronic design automation in the chip industry. In the 1980s, designing a microprocessor required a team of fifty or more engineers manually placing transistors. By the 2000s, EDA tools had automated most of that work, and a team of five could design a chip that was more complex than anything the team of fifty could have produced. Did chip design employment collapse? It grew three to five times over the same period. Because when the bottleneck is human attention, removing it doesn’t eliminate demand — it unleashes it. This is Jevons Paradox, one of the oldest principles in economics: when you make something more efficient, people use more of it, not less.

This is what happened with AlphaFold, DeepMind’s protein-structure prediction system. Before AlphaFold, determining the 3D structure of a single protein took months or years of painstaking lab work. AlphaFold predicted the structures of 200 million proteins in a matter of months. Did structural biologists become unemployed? Three million researchers now use AlphaFold’s predictions as starting points for work that wasn’t even conceivable before. The bottleneck was prediction. Remove it, and the work moves to interpretation, application, and the next harder question.

Paul Welty, who used autoresearch-style systems for semiconductor research, described the feeling with a sentence that should be tattooed on the forearm of every knowledge worker in the world: “The machine was waiting on me.”

Not the other way around. The machine was waiting on him. His expertise, his judgment about what to optimize and how to evaluate it, was the scarce resource. The computation was abundant. The human wisdom about how to direct it was the bottleneck.

Hallucinations as Serendipity

There’s a deeper current here, one that runs beneath all these examples, and it connects to something Sakana AI — a Tokyo-based research lab — published earlier this year. They built a system called DiscoPop that autonomously discovered a genuinely novel optimization algorithm — something no human had designed before. The researchers described the discovery mechanism with a phrase that stopped me cold: “hallucinations as serendipity.”

AI systems hallucinate — they generate plausible-sounding things that aren’t true. This is, in most contexts, a flaw. If you’re asking for medical advice or legal citations, hallucination is dangerous. But in a research context, inside a well-designed arena with rigorous testing, a hallucination is just... an idea. A weird, unexpected, potentially wrong idea that gets tested against reality within minutes. Most of those ideas fail — witcheer’s 74 percent failure rate proves that. But some of them work. And the ones that work are, by definition, ideas that no human thought of, because they emerged from the stochastic weirdness of a language model doing something it wasn’t strictly designed to do.

Sakana pushed this idea to its logical conclusion with ShinkaEvolve, a successor to DiscoPop that tackles the exact “low creativity” problem the autoresearch community kept bumping into. Autoresearch is a single climber exploring one mountain — make a change, test it, keep or discard, try again. ShinkaEvolve populates the entire mountain range with climbers who share notes and breed new routes. It maintains a population of candidate solutions that compete, recombine, and get filtered for genuine novelty — evolution, not hill-climbing. The results back up the architecture: novel loss functions that outperform hand-designed ones, competitive programming problems solved at tournament-level performance, state-of-the-art circle-packing solutions in roughly 150 evaluations where prior methods needed thousands. The lesson is the same one we’ve been tracing all along: the ceiling isn’t the agent’s intelligence. It’s the design of the arena. A single-agent loop and an evolutionary population are two different arenas, and the architecture of the arena determines what can emerge from it.

This is the creative potential of arena design. Not creativity in the AI itself — we’ve established that the system is, at its core, doing educated brute force — but creativity in the output of a system that explores a possibility space no human could cover manually. The creativity is in the coverage, not the insight. A human researcher might try twenty variations on a learning rate schedule. An autonomous system tries seven hundred. Somewhere in those seven hundred is a combination no human would have guessed, not because the AI is brilliant but because it was tireless and the arena was well-built.

Udit Goenka demonstrated how far this pattern can stretch when he published a Claude Code “skill” — essentially a reusable arena template — that could be adapted to any domain. The project gained 608 stars on GitHub, not because the code was extraordinary, but because people recognized the meta-pattern: once you learn to design arenas, you can design them for anything. Marketing optimization. Code refactoring. Legal document review. Product testing. The arena is the transferable skill. The domain is just the content.

The Dinner-Party Version

So here’s the dinner-party version. The version you retell to a friend over wine, stripped of all the technical detail, reduced to its essential shape:

There’s a guy named Andrej Karpathy. One of the most respected AI researchers alive — co-founded OpenAI, led AI at Tesla, Stanford PhD, over a million YouTube subscribers. He built a tiny program, 630 lines of code, and put it out for free. What it does is simple: it runs experiments while you sleep. You tell it what you’re trying to improve, you tell it what counts as better, you tell it what it’s allowed to try, and you go to bed. You wake up and it’s run a hundred experiments and found improvements you didn’t think of.

The internet lost its mind. 31,000 people starred the project in five days. But here’s the thing: the breakthrough wasn’t the code. The code is trivial. The breakthrough was the idea — that the most important skill in AI isn’t asking good questions. It’s designing the system so you don’t need to be there at all. The bottleneck in every AI system right now is the human. Remove the human from the loop, and the system runs a hundred times faster.

But — and this is the important part — “remove yourself from the loop” doesn’t mean “become irrelevant.” It means the opposite. It means your job shifts from doing the work to designing the arena the work happens in. That’s harder, not easier. It requires deeper expertise, not less. Because a bad arena produces bad results at machine speed, which is much worse than bad results at human speed.

It’s the McDonald’s principle. Ray Kroc didn’t make a single French fry. He designed a system where anyone could make a perfect French fry. That’s the highest-leverage position in any organization, and it’s about to become the highest-leverage position in knowledge work.

The Machine Is Waiting on You

Let me close with Karpathy’s own words, from the Sarah Guo conversation, because they capture the shift more precisely than I can paraphrase:

“To get the most out of the tools that have become available now, you have to remove yourself as the bottleneck. You can’t be there to prompt the next thing. You need to take yourself outside. You have to arrange things such that they’re completely autonomous. The name of the game now is to increase your leverage. I put in just very few tokens just once in a while and a huge amount of stuff happens on my behalf.”

“I don’t want to be the researcher in the loop, looking at results. I’m holding the system back. So the question is, how do I refactor all the abstractions so that I’m not? I have to arrange it once and hit go. The name of the game is how can you get more agents running for longer periods of time without your involvement, doing stuff on your behalf.”

Read those sentences carefully. This is not a man who is worried about being replaced by AI. This is a man who is worried about slowing AI down. His expertise is not threatened by the machine. His expertise is the thing the machine is waiting for. He is the bottleneck, and he knows it, and his entire strategy is to extract his judgment from his moment-to-moment presence and embed it into the structure of the system itself.

That extraction — taking what you know and encoding it not as instructions you give in real time, but as the design of an arena that runs without you — is the skill of the next decade. It is not prompting. It is not “AI literacy.” It is not learning to use ChatGPT better. It is the ability to look at a problem and ask: What would this look like if I didn’t need to be here?

Every McDonald’s in the world makes the same French fry. Not because someone is standing over every fryer, monitoring every batch, adjusting every timer. Because someone, once, designed a system so good that the quality of the outcome stopped depending on the presence of any particular person.

Your knowledge — your expertise, your judgment, your taste — is the most valuable thing you have. The question is no longer whether you can use AI to amplify it. The question is whether you can encode it into a system that runs while you sleep.

The machine is waiting on you.

But it won’t wait forever.

AI Thought Leader School — The Agentic Workspace

We are living through one of the biggest shifts in how knowledge work actually gets done. The move from AI as a chat tool to AI as a full agentic workspace isn’t theoretical anymore — it’s happening now, and the thought leaders who understand it early are going to operate at a level that simply wasn’t possible before.

AI Thought Leader School exists for exactly this moment. Each session, we go deep on the ideas, tools, and workflows that matter most for building influence in an AI-first world. This isn’t a course about prompting tips or tool reviews. It’s about fundamentally rethinking how you collect information, process it, generate original ideas, and turn those ideas into work that moves people. If you’re serious about building a body of work and an audience that trusts you, this is the room to be in.

During this class, we:

• Explored the shift from the chat paradigm to the agent paradigm

• Discussed how AI now fundament…

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We have 30+ speakers joining us from 9:30am-5:30pm EST, and all you need to do to join any session is register for free. Each session will include screen shares that take you behind the scenes of creators’ workflows, as well as an AI prompt/skill share.

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Why I Co-Founded Cozora

You can't keep up with AI alone. Neither can we.

The only way to keep up is to build a network of relationships with others tinkering on the frontier.

That's why we built Cozora.

Together, Claudia, Joel, and I have spent hundreds of hours identifying top AI creators, vetting them, building relationships with them, bringing them into a single community, and creating a win-win container for us all to share our expertise.

You can leverage all the work we’ve done and tap into the network's expertise for a fraction of the time and cost.

By the end of the year, hundreds of creators will be part of the community.

Registering for free for the live summit this Friday is the best way to get started with Cozora if you’re not already a member.

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What Cozora Members Receive

• Recordings From The Summit. Members receive recordings of all the summit sessions, along with the prompt and skill shared.

• Weekly AI Masterclass. Every Thursday at 11:00am-12:30pm EST, we offer members a live, interactive class where one of the AI creators walks you through their workflow and shares one of their top prompts. I co-host all of the classes.

• Crowdsourced Reports. We create a monthly crowdsourced report in which creators share their top tools, pro tips, prompts, and skills across various topics.

Join Cozora

20%-50% Discount Codes To Join Cozora (For Paid Members Of This Newsletter Only)

If you’re not already a member, you can become a member here. Then, simply scroll to the bottom of this page, and you’ll see the discount codes because the paywall will be gon.

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AI-Native Content Creation

Part 1: The Marketing Hook

Something fundamental has shifted in how content gets made — and most people haven’t caught up yet.

For years, the dream of publishing at scale was bottlenecked by time, expertise, and the sheer cost of production. Hiring writers, booking studios, fact-checking thousands of sources — it all added friction that kept most creators stuck at one piece a week, if that.

That bottleneck is gone.

In this class, I walked through what I’m calling the **AI-Native Content** model — a new approach to producing articles, podcasts, and media that doesn’t just use AI as a writing assistant, but as a full creative partner in the process. Not AI slop. Not ghostwritten content awkwardly passed off as your own voice. Something different: a hybrid model where your curation, taste, and judgment do the heavy lifting — and AI handles the production.

The result? I’m seeing 4–5x output increases in my own content. One-shot articles I’d releas…

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We’re going to cover:

• Wyndo’s top skills in Claude Code

• How to use Obsidian as your AI Second Brain

• How to integrate NotebookLM into Claude

• How to integrate NanoBanana into Claude

• The implications of Auto Research

• To measure and improve skills

• How to bring your entire Google Workspace into Claude Code

If you’ve ever wanted to see what’s possible and practical with today’s tools, then this session is for you! There’ll be lots of screensharing and looking over Wyndo’s shoulder as he solves problems in ways that weren’t possible just a few months ago.

Date & Time

• Time: 11:00am-12:30pm EST

• Date: Tuesday, March 24

How To Participate

• Attend Substack Live: Click on this link today at 11:00am EST

• Get Recording: I will post the recording later this week for paid members.

Click To Join Live At 11am EST On Mar 24

See you soon!

Michael