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The Multiplier Effect

Mentorship Program Mentor 2024-Present

Impact

  • 5-6 active mentees
  • 10+ AI agents projected by Q3 2026
  • Cross-industry domains

Skills

Mentorship AI Education Multiplier Effect

The Multiplier Effect

The Challenge

If my thesis is true—that domain expertise + AI tools + discipline = production capability—then it should be replicable. One person building AI systems is an anecdote. Multiple people across different domains applying the same framework is evidence.

I needed to prove this wasn’t just about my specific skills or circumstances.

The Constraint

  • Varied backgrounds: Mentees from product, marketing, operations—not engineering
  • Different domains: Each person brings unique expertise, faces unique problems
  • Limited time: Mentorship alongside full-time work and personal projects
  • No formal program: Informal relationships, not structured curriculum

The Approach

Selection Criteria: Not looking for people who want to “learn to code.” Looking for:

  • Deep domain expertise in their field
  • Specific problems they want to solve
  • Willingness to iterate through failure
  • Patience for the learning curve

Framework Transfer: Teaching the same pattern I use:

  1. Start with the problem, not the technology
  2. Use AI as teacher AND tool—learn by building
  3. Ship incrementally, validate constantly
  4. Accept that most first attempts fail

Ongoing Support:

  • Weekly check-ins during active build phases
  • Architecture review and debugging sessions
  • Encouragement through the “this will never work” moments
  • Celebration of wins, post-mortems on failures

Current Cohort

5-6 Active Mentees:

Representing diverse domains:

  • Operations professional building workflow automation
  • Marketing leader developing content intelligence tools
  • Product manager creating customer research automation
  • Business analyst building data pipeline solutions
  • Consultant developing client delivery frameworks

Each is working on AI systems relevant to their domain expertise—problems they understand deeply that generic tools don’t solve.

Projected Outcomes

By Q3 2026:

  • 10+ AI agents deployed across mentee projects
  • Multiple production systems generating measurable value
  • Documented case studies of non-technical builders shipping AI

What Success Looks Like:

  • Each mentee capable of building and shipping independently
  • Problems solved that wouldn’t have been solved otherwise
  • Framework validated across multiple domains and contexts

What This Proves

The multiplier effect is the ultimate validation of the thesis.

If only I can do this, it’s interesting but not significant. If others with no engineering background can apply the same framework and achieve similar results, that’s evidence of a broader shift in what’s possible.

We’re still early in this experiment. The mentees are at various stages—some have shipped, some are still building, some are learning to walk before they run.

But the early signals are encouraging. Domain experts are building things. Problems are getting solved. The barrier to software creation is demonstrably lower than conventional wisdom suggests.

The question isn’t whether this scales. It’s what happens when it does.

For Potential Mentees

I’m not currently accepting new mentees (bandwidth constraints), but I document the framework and learnings publicly:

  • Follow the Journal for ongoing insights
  • Watch for future group programs
  • Reach out if you’re applying this framework and want to share learnings