Topics
Recurring themes run through the series. Each is a lens on the same shift — from treating AI as a clever tool to engineering the systems that make it reliable.
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Coherence
Why AI pays off only when it and the systems beneath it reinforce each other — and why pointing it at cost-cutting caps the return before you start.
4 essays
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The foundation
Why enterprise AI failures are failures of structure, not intelligence — the seams where AI meets your systems, and the handoffs between steps, are where value is won or lost.
9 essays
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Trust and authority
Why capable AI still isn't AI you can stand behind — trust is a bounded grant of authority with a name attached, not a verdict on competence, and real control prevents the wrong action instead of reporting it after the fact.
8 essays
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Vibe coding vs. reliable systems
The paradigm shift at the heart of the series: from typing into a chat window and hoping, to engineering the systems that make AI reliable.
5 essays
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The harness
The engineering substrate around a coding agent — specifications, validation, workflow, context — that turns probabilistic output into production-grade software.
15 essays
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Specify. Direct. Validate.
The loop that replaces reactive prompting. Write the spec, direct the agent against it, validate the output — then fix the spec, not the code.
5 essays
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Architect-CEO
How leadership roles change when agents execute. Engineers define outcomes and validate them; the organization writes software differently.
6 essays