Field notes from four reinventions — a $200M exit, two unicorns, and building AI-native. Published as I learn.
AI regulation is retreating in 2026, but enterprise buyers still gate revenue on trust. The AI governance stack: how to build governance as a moat, not a tax.
Speed to lead decides who wins the deal. The average company takes 42 hours to reply and loses to whoever answered first. How to close the response gap.
The AI productivity paradox: more tools and faster output, yet you ship less. Why AI floods your unfinished work, and how to finish more by starting less.
AI product pricing is breaking as agents collapse seat counts. Why per-seat models fail, and a framework to move from seats to outcomes without killing revenue.
When AI can build anything, taste becomes the last moat. Why taste is a trainable skill, not a gift, and the exact system founders use to build it on purpose.
Loop engineering, not prompts, is the real moat in AI agents. A field guide to the loop ladder, the verification gap, and building agents that stay reliable.
The one-person company is real. Here is the operating model solo founders use to reach company-scale revenue, plus the four ceilings that still break it.
Agent delegation, not model quality, decides whether your AI ships or stalls. A builder's framework for when one agent beats a swarm and how to grant autonomy.
AI now does the thinking for you, and cognitive debt is the bill: the judgment you quietly lose by offloading it. Here is what founders should never hand over.
AI products average a 52% gross margin, not the 80% that SaaS promised. Here is why the playbook breaks, where the margin leaks, and how to win the points back.
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