Philosophy
My background is in economics, computer science, and applied statistics — fields that ultimately deal with the same problem: allocating scarce resources efficiently and making decisions under uncertainty. David Ricardo's theory of comparative advantage describes trade, but it also applies to how individuals allocate their time. When Ethan Mollick showed that knowledge workers using AI complete tasks significantly faster, the implication was clear: the marginal cost of learning has dropped, and effort should shift toward higher-judgment work.
I use AI as a learning multiplier. It allows me to move through unfamiliar domains faster — not by skipping understanding, but by reaching the parts that require judgment sooner. At the same time, it eliminates low-leverage work: boilerplate, syntax recall, repetitive debugging, and documentation lookup. Those tasks don't require deep thinking, so I offload them.
The result is a simple operating model: I spend my time either building, or learning so I can build better. Everything else is optimized away.
Knowledge Acquired
r = 7 · α = 1.8
All three lines start at the same baseline. The flat line represents no active learning. Traditional learning grows steadily but linearly — the same amount each month. AI-augmented learning compounds: each thing you learn lowers the cost of learning the next, so the gap widens every month rather than staying fixed.