Almost all major disruptive technologies follow a predictable pattern of evolution:
#1. Reproduction phase
People simply reproduce what came before but with the new technology. For example, the very first New York Times website was essentially a copy of the print sections, with limited interactivity, no comments and no blogs:
#2. Hybrid Phase
In the hybrid phase, we begin to see innovations that are only possible because of the new technology while still holding on to the past. My favorite example is that the first cars were designed to look like stage coaches, and they even had whip holders.
Fun fact: The reason for the whip holder was that if the car broke down, it could still be connected to a horse.
#3. Reimagine Phase
In the reimagine phase, we see new products that are built from the ground up without any of the old assumptions.
For example, think about music before the Internet. Music was sold in albums. Today, most music is consumed through music subscriptions on mobile devices. And, listeners can listen to virtually any song ever created.
#4. Maturity Phase
In the maturity phase, we continue to see more creativity, reliability, scalability, and standardization of the reimagined business models and products that succeed.
How This Model Relates To AI
Knowing that this model exists is helpful, because it helps give us intuition about how the future of AI may unfold. More specifically, it helps us:
Create AI-first products that reimagine what’s possible
Create strategies designed for where the puck is going
Unseat incumbents who have trouble thinking from scratch because of sunk costs
What We Covered In Class
In this first class, I introduced students to various models and case studies of AI-First thinking. More explicitl, we:
Explored how AI can help transcend traditional human limitations in learning and thinking
Examined the "horseless carriage syndrome" and its impact on AI adoption
Analyzed how AI enables mapping vast knowledge domains across multiple disciplines
Discovered techniques for identifying multi-dimensional blind spots in thinking
Investigated advanced AI tools like NotebookLM for synthesizing diverse sources
Explored how AI can process 250+ thought leaders' content simultaneously
Learned strategies for breaking out of closed mental feedback loops
Discussed transforming from linear to recursive, multi-order thinking
Examined real examples of AI-first approaches to research and analysis
Participated in breakout discussions about overcoming cognitive limitations
What You Get In Today’s Post
Free subscribers get access to:
The first 30 minutes of the class
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The entire class
Class resources
Summary Of Key Takeaways
Action Plan
Presentation Slides
Resources Shared
Time Stamps
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