Non Linear Learning

Linear learning is not working. Time to embrace non-linear learning.

But here's the catch: non-linear learning done non-linearly doesn't yield better results because the brain still needs step-wise connections to perceive and encode information. The missing piece isn't abandoning linearity but it's knowing where to apply it.

The 80/20 Shift

The learning process has always been: Discovery → Access → Perception (Consumption, Categorization, Connection, Creation).

In the era of information scarcity, we spent 80% of our time on discovery and access. Hunting for the right sources, getting our hands on quality information was a thing. The first principles didn't change, but finding and accessing information felt like a job by itself. AI removed that bottleneck overnight.

Now discovery and access take 20% of our effort. The real work happens in the other 80%. the 4Cs of perception to be precise. But most people are still optimising for the old game, spending months researching "the best way to learn X" instead of actually learning X.

Why Pure Non-Linear Fails

Information is now available in multiple genres, formats, and entry points. This abundance makes discovery harder because you don't know what to pick, what to leave, what's useful now, and what's useful later. Pure non-linear learning creates chaos. You jump from concept to concept without building the step-wise connections your brain needs. You accumulate fragments but never achieve coherence.

The brain's perception and encoding mechanisms are inherently linear. Neural pathways build sequentially. Understanding emerges through connected, not scattered, insights.

The Altitude Method: Structured Non-Linearity

The solution is to embrace non-linearity in discovery while maintaining linearity in perception. This happens through altitude-based learning.

30,000 ft (Strategic Overview): What is this domain made of? Identify 5-7 major components. Build your structural map first.

3,000 ft (System Architecture): How do these components connect? Understand relationships, dependencies, and trade-offs between building blocks.

3 ft (Implementation Details): Dive deep into specific components, but only after understanding their context and connections.

Multiple altitudes solve the genre problem. At 30,000 ft, you consume industry reports and strategic frameworks. At 3,000 ft, you study technical architectures and case studies. At 3 ft, you examine code, detailed processes, and implementation specifics. Each altitude has its optimal information format. No more confusion about what to pick since the altitude determines the genre.

The 4Cs at Each Altitude

Consumption: Deliberately choose information appropriate to your current altitude. Fight the urge to jump between levels.

Categorization: Sort insights by altitude. Park off-level information in your "altitude parking lot" instead of switching contexts.

Connection: Build relationships between concepts at your current level before moving up or down. Neural encoding happens through connected understanding, not isolated facts.

Creation: Generate output at each altitude (strategic summaries at 30,000 ft), architecture diagrams at 3,000 ft, implementation guides at 3 ft.

The Complete Learning Equation

Traditional learning: Learning as objective → Knowledge as outcome

AI-era learning: Learning as objective → Artifact as output → Understanding as outcome

Creation isn't optional. It's where understanding crystallizes. This method naturally gives rise to books - actually, multiple books at different altitudes for different audiences. When you can explain something at 30,000 ft for executives, 3,000 ft for practitioners, and 3 ft for implementers, you've achieved genuine comprehension.

This isn't a new insight. Waldorf education has always emphasized creating your own books as the core philosophy of learning. Students don't just consume textbooks, they author their own knowledge through the act of synthesis and creation. This first principle continues to work even in the AI era. Nothing is changing at the first principle level. It's our methods and ways that are evolving. The artifact becomes your external brain, freeing your internal cognitive resources for higher-order thinking. You're not just learning, you're building reusable knowledge assets that compound over time.

Why This Works Now

AI handles information retrieval brilliantly, but it can't synthesize understanding across altitudes for you. It can't decide which altitude you should be operating at, or when to switch between them. The valuable human skill becomes: strategic altitude selection, cross-altitude synthesis, and question formulation that drives meaningful inquiry.

Non-linear discovery leverages AI's strengths. Linear perception respects your brain's architecture. The altitude framework bridges the gap between chaos and structure.

Actionable Implementation

Pick any complex subject you're learning. Ask three questions:

1. What does this look like at 30,000 ft?

2. What are the key building blocks at 3,000 ft?

3. Which component deserves deep investigation at 3 ft?

Use AI to explore each altitude systematically. Create an artifact at each level. Notice how understanding compounds across altitudes instead of competing with them.

The future belongs to those who can learn non-linearly while thinking linearly. The altitude method gives you both.

Linear learning is dead. Structured non-linear learning is the future.

Here is one of the many artifacts of my AI learning: https://mimic-render-lab.lovable.app/

I must say this was the most fun I ever had in learning a complex subject outside my comfort zone. I can't wait to create an artifact for learning products at ServiceNow 🔥 I am not saying this is a lean method. It is not. But this is a durable method for learning anything complex.