Essays & insights on ai & technology.
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.
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.
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 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.
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.
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 vendors and labs sell reliability scores they cannot back. Eval-washing is how a benchmark hides the truth, and how a founder can audit any AI claim.
AI made output nearly free, so volume metrics lie. Cost per correct task is the only AI productivity metric that survives. Here is how to measure and use it.
Founders treat evals as overhead until the reliability tax comes due. How to size an AI eval budget that turns invisible failures into a bounded line item.
AI leaderboards are gamed, contaminated, and quietly retired. The Benchmark Contamination Playbook: how to pick the right AI model without getting fooled.
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