Lessons, frameworks, and stories from the trenches of building companies.
AI compute costs are set by a capital cycle you do not control. The Subsidy Line, the three price regimes, and the two-way stress test every founder needs.
AI free tier economics changed the rules: every free user burns inference. A framework for pricing, capping, and designing a free tier that pays for itself.
When anyone can build anything, the scarce skill is knowing what to build. Here is a founder's operating system for selection judgment in the age of AI.
Vertical AI wins because intelligence is a commodity and the wedge is the moat. How to pick one narrow vertical, price it against the wage bill, and own it.
AI can recall any fact instantly, so the value of knowing did not go to zero. It inverted. Here is which knowledge to stop learning and which to own cold.
Every AI feature has a countdown to the day the model ships it for free. Here is how to tell how long your product has, and how to build above the waterline.
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 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.
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