Abundance Engineering

After a long break from writing, I’m back with an experiment.

For the past two years, I’ve been building AI systems and thinking deeply about what separates implementations that create abundance from those that just automate people away. I call this approach Abundance Engineering, and this Substack is where I’ll share what I’m learning. The longer, deeper version of what I post on LinkedIn.

The paradox that changes everything

There’s a counterintuitive economic principle: when you make something cheaper or more efficient, people don’t use less of it. They use more.

When cars became more fuel-efficient, people didn’t drive less. They drove more. When cloud computing made storage cheaper, companies didn’t store less data. They stored exponentially more.

This is Jevons Paradox, and it’s the key to understanding how AI creates abundance instead of scarcity.

What is Abundance Engineering?

When AI makes analysis cheaper, doctors don’t do less analysis. They can finally analyze every patient interaction deeply and spend 3x more time on direct patient care.

When AI makes financial modeling faster, analysts don’t build fewer models. They can test 20 scenarios instead of 3 and spend more time on strategic recommendations.

When AI makes performance review paperwork trivial, managers don’t do less feedback. They can finally give the thoughtful, personalized coaching that was previously impossible at scale.

The pattern: AI handles the repetitive work. The valuable work expands. Humans focus on judgment, creativity, and connection. That’s Abundance Engineering. Deliberately designing systems where AI compression creates human expansion.

Why this matters now

The AI conversation is dominated by two extremes: unbounded hype or existential doom. Both miss what’s actually happening in production systems.

I’ve seen performance reviews go from 2 weeks to 2 days. Managers spend more time on meaningful feedback. An engineer programmatically generated our help center articles. Our GTM team focused on growth initiatives instead. Product engineers spend more time on user experience. AI handles the boilerplate. None of these eliminated jobs. They all made the work more human.

But abundance isn’t automatic. You have to engineer it deliberately. You have to understand what AI is actually good at (and terrible at). You have to design systems where compression in one area creates expansion in another.