All thesesPattern Lab
activev3.0 · 28 signals

Pattern Lab (Meta)

The meta-thesis: how to discover, test, and refine macro patterns. The methodology behind the methodology—pattern catalysis as a practice.

Pattern Lab (Meta)

The Core Argument

Pattern Lab isn't just a website—it's an experiment in collaborative thinking. The hypothesis: by thinking in public and inviting challenge, ideas evolve faster and more robustly than in private.

Key Claim: Thinking in public accelerates learning. The feedback loop from "tell me why I'm wrong" produces better thinking than private contemplation.

The Three-Layer Signal Chain

Every signal I track gets processed through three lenses:

Layer 1: Futurist

  • What pattern does this signal reveal?
  • Is this a leading indicator of a larger trend?
  • How does this connect to other signals I'm seeing?

Layer 2: Investor

  • How does this affect my investment theses?
  • What asset classes, sectors, or companies are impacted?
  • What's the time horizon for this to matter?

Layer 3: Owner

  • What does this mean for business owners and operators?
  • What actions should they take?
  • How does this create or destroy value in their businesses?

Most analysis stops at Layer 1 or 2. The Owner layer is where I try to add unique value—translating macro patterns into operational implications for people running businesses.

The Four Modalities

Pattern Lab content flows through four modalities:

  1. Theses — The core arguments, versioned and evolving
  2. Signals — Evidence that supports, challenges, or evolves theses
  3. Network — The connections between theses that reveal higher-order patterns
  4. Newsletter — Weekly synthesis for subscribers

Pattern Catalysis

I don't claim to predict the future. What I do is:

  1. Collect signals — Obsessively gather evidence from diverse domains
  2. Detect patterns — Look for rhymes across seemingly unrelated areas
  3. Synthesize theses — Articulate what the patterns mean
  4. Invite challenge — Ask smart people to tell me why I'm wrong
  5. Evolve — Update theses based on new evidence

This is "pattern catalysis"—I'm not creating the patterns, I'm accelerating their recognition.

Why This Works

Cross-domain pollination — My background (GovCon operator, investor, geopolitics observer) lets me see connections that specialists miss.

Skin in the game — I invest based on these theses. Wrong ideas cost me money.

Community intelligence — The signals submitted by readers are often better than what I find myself.

Versioned thinking — The git history of each thesis shows how my thinking evolved. Transparency builds trust.

What I'm Watching

1. Community engagement — Are people submitting signals? Are the signals high quality?

2. Thesis accuracy — Am I getting more right over time?

3. Owner value — Are business owners finding the Owner-layer analysis useful?