Field notes from four reinventions — a $200M exit, two unicorns, and building AI-native. Published as I learn.
AI can now do almost anything, yet trust in its output just hit a record low. Here is a founder's framework for when to trust AI output and when to verify.
Hire vs automate is not a cost question. Here is a founder's framework for which work to hand an AI agent, which to keep human, and why the line matters.
AI agents are becoming the buyer, not just the tool. Here is how founders build software a machine customer can find, trust, and actually choose to use.
AI made gathering and drafting nearly free. The one skill it still cannot do, synthesis, is your real edge. What synthesis is, and how to build it as a founder.
AI token prices keep falling, yet AI bills keep climbing. The efficiency trap explains why cheaper compute compresses margins, and the discipline that fixes it.
Spec-driven development flips the source of truth from code to the spec. Why the spec is now the unit of work, and how to write one an AI agent can ship.
One person with AI does the work of twenty while enterprises spend billions on deployment help. The AI adoption paradox, mapped, plus the founder playbook.
AI platform risk, explained for founders: why AI vendors climb into their customers' markets, plus the dependency map and moat design that survive stack creep.
AI now does the junior work that used to turn beginners into experts. Inside the apprenticeship gap, and the system founders use to keep forming real skill.
Most AI pilots never reach production. The Production Gap explains why demos die before launch and the five gates every founder has to cross to ship real AI.
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