AI Agent Memory: What to Keep, What to Forget
AI agent memory is the layer founders skip. The model forgets at every call, so the real skill is not remembering everything, it is choosing what to forget.
Field notes from four reinventions — a $200M exit, two unicorns, and building AI-native. Published as I learn.
AI agent memory is the layer founders skip. The model forgets at every call, so the real skill is not remembering everything, it is choosing what to forget.
Service as software means selling work, not tools. The accountability ladder shows which rung keeps software margins and which makes you a services business.
AI made building cheap, so skill is no longer your ceiling. The real constraint is the ambition gap, and most people bank the savings instead of aiming bigger.
Most teams plan the launch of an AI agent and never plan the firing. Here is the agent tenure model, the graded path from probation to a clean decommission.
Most founders think their data is a moat. Most of it is a cost. Here is the data moat test, four conditions that separate a real asset from expensive exhaust.
Founders optimize AI cost and quality but ignore availability. Here is the AI capacity risk, why it is structural, and how to keep serving under real load.
AI makes learning feel fluent while blocking the skill you think you are building. Here is the fluency trap, the science behind it, and how to escape it.
AI agent reliability falls as autonomy rises. Here is the tradeoff most builders miss, why the most reliable agents are barely agents, and how to ship them.
AI hands you the average of everything good. Taste is how you beat the average. Here is the buildable anatomy of taste, and why the skeptics are half right.
The AI boom runs on a half-trillion-dollar capital stack built on circular financing. How to read the stack, trace the money, and find the capital-light lane.
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