Announcing The AI-First Thought Leadership Course (Save The Date)

TL;DR

Announcing a 4-class course for subscribers:

• Free subscribers get:

  • First 20-minutes of each class

  • AI podcast summary for each class

• Paid subscribers also get:

  • Access to all live 90-minute classes

  • Full on-demand classes

  • Detailed class takeaways

Course Overview

• 100x AI opportunities won’t come from just optimizing your existing processes.

• They’ll come from reinventing your entire process based on first principles.

• The challenge is it’s hard to understand what reinvention looks like in advance.

• Over the last few months, I’ve been studying disruptive innovation from previous technological epochs, extracting the patterns of reinvention and paradigm shift, and completely overhauling my entire process for thought leadership.

• During the course, I’ll share my screen and overview of my new system step by step.

• More specifically, I’ll share how:
  

  • I built an AI research tool. Every day, it extracts the text of 200+ blogs, X posts, newsletters, academic papers, and podcasts. Then it extracts and summarizes the relevant parts. This helps me avoid manually visiting all of the sources, and it helps me blow past the bottleneck of consuming content at a 200-word-per-minute rate.
    

  • I think with mental models. I’ve built 10+ Claude projects I use every week to do deep analysis on any topic. This gives me the fodder for creating rare and valuable ideas. You’ll see how I come up with insights in minutes that previously took me dozens of hours.
    

  • Why my new audience is also AI. Reinvention isn’t just about changing workflow, it’s about changing paradigms. For example, when I write articles now, I'm actually thinking that longterm my largest audience will be AI learning agents reading my articles on behalf of learners. This subtly shifts the entire logic of thought leadership.
    

  • I co-author articles with AI. You can see this in two of my recent articles: The Consciousness Effect and The Ghost In The Pattern. Using this approach, I’ve been able to publish in-depth, high-quality articles in a few hours rather than dozens of hours.
    

  • I’m building a tool to generate and test 1,000x the quantity of thought leadership ideas. This tool is in the early stages, but I’ll show you how testing ideas will be fundamentally different in the near future. Long story short, testing ideas by throwing them up on social media is extremely inefficient. Now is the time to understand and apply the 10,000-Experiment Rule.
    

  • And much more…

Who It’s For

Knowledge workers, entrepreneurs, and thought leaders who want to:

• Be on the frontier of AI.

• Get a glimpse of what AI-First Thought Leadership will look like.

• Understand a path to leapfrog other thought leaders and become the recognized expert in their niche.

Curriculum At-A-Glance

• AI-First Intro (January 13

• AI-First Research (January 21)

• AI-First Thinking (January 28)

• AI-First Ideation (February 4)

• AI-First Publishing (February 11)

Get Access

To access the full classes, all you have to do is be a paid subscriber. Then, you’ll get reminders automatically. To lock in the $10/month rate forever before it increases to $15/month next Monday, December 9, sign up now:

With that said, below is the deeper download on why I’m launching this AI-First series…

The Reinvention Trap: Why "Better" Isn't Always Better

"If I had asked people what they wanted, they would have said faster horses."
—Henry Ford

In 1905, if you'd asked the world's smartest transportation experts how to build a faster transportation system, most would have suggested breeding stronger horses or building better carriages. A decade later, those same experts watched Ford's Model T revolutionize how humans move.

This pattern repeats throughout history.

When transformative change arrives, it rarely comes from making existing systems better. It comes from fundamental reinvention. Yet most of us, when faced with new technology, instinctively ask: "How can we use this to make our current process more efficient?"

This is the reinvention trap – and it's killing your ability to truly innovate with AI.

Most AI knowledge workers do some variation on the following when adopting AI:

1. Understand the existing workflow

2. Use AI to make it more efficient

While it helps in the short term, it hurts in the long term. And the following Stephen Covey quote explains why:

“If the ladder is not leaning against the right wall, every step we take just gets us to the wrong place faster.”
—Stephen Covey

The two AI ladders are optimization and reinvention.

• Optimization means making your existing approach faster.

• Reinvention means thinking from scratch with an AI-first mindset.

Doing optimization alone is tempting because the rewards are obvious, faster, and easier. Reinvention is harder and longer, but has a much larger payoff:

History shows us that the biggest breakthroughs come not from optimization, but from fundamental reinvention…

The Optimization Trap

For example, below are some historical examples of where people initially focused on making existing systems "better" rather than reinvention:

1. The Early Internet Document Era: Companies spent years perfecting ways to put paper documents online as PDFs. They built complex document management systems and scanning workflows. But this missed the revolutionary potential of interactive websites.

2. Early Factory Electrification: When electricity first became available, factory owners simply clustered their machines around the new electric generator – just as they had around steam engines. They missed the opportunity to completely reimagine the factory layout around the assembly line.

3. Early Television: The first TV shows were filmed radio programs. Producers focused on improving audio quality and adding basic visuals rather than exploring the unique storytelling possibilities of the medium.

The Power of Reinvention

Now contrast these with examples where people fundamentally reimagined what was possible:

1. TikTok's Music Revolution: Instead of just putting songs online, TikTok transformed music hooks into the background of short-form video storytelling. This wasn't just a better way to share music – it was a new form of creative expression that changed how music is created, marketed, and consumed.

2. Wikipedia vs. Traditional Encyclopedias: Rather than making encyclopedia writing more efficient, Wikipedia reimagined knowledge creation as an Internet-enabled collaborative, living process. They didn't just make a better encyclopedia; they created a new way of documenting human knowledge.

3. Cloud Computing: Amazon didn't just build better servers – they reimagined computing as a utility service. This wasn't just more efficient IT; it was a fundamental shift in how we think about computational resources.

The Lesson For AI Innovators

The pattern is clear.

The biggest breakthroughs come when we stop trying to make existing processes more efficient and instead ask fundamental questions:

• What if we completely reimagined how this works?

• What constraints are we accepting that no longer apply?

• What would this look like if we built it from scratch today?

Said differently, when facing technological change, beware of the reinvention trap.

❌ Don't ask: "How can we do this better?"
✅ Ask: "What if we did this differently?"

The next time you're tempted to optimize your current workflow, pause and consider:

Are you making a better horse and buggy, or are you inventing the car?

The Cost of Playing It Safe

The greatest risk in times of change isn't being too radical – it's being too conservative. Kodak invented the digital camera but buried it because they were too focused on making better film. Blockbuster could have bought Netflix for $50 million but thought improving their store layouts was the path forward.

The future doesn't belong to those who make things better. It belongs to those who make them different.

Subscribe Now

This is your chance to be on the frontier of the future of work. Hope to see you in the course in 2025.