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AI Engineering: AI as a reviewable tool.

Agent workflows, evaluation harnesses, guardrails, permissions, logs, and the patterns that keep AI-assisted work auditable instead of opaque.

Articles

  1. When Code Gets Cheap, Coordination Gets Expensive

    AI-assisted development is making software easier to generate, but not automatically easier to ship. The next bottleneck is engineering management: ownership, review, decision rights, platform maturity, and absorbing machine-rate work without creating human-rate chaos.

  2. AI Didn't Make Your Company Faster. It Made Your Failure Scalable.

    AI genuinely speeds up individual work. But an organization is not the sum of its productive individuals. When decision-making, ownership, review, integration, and trust stay slow, AI doesn't remove your bottlenecks. It makes them visible, and then scalable.

  3. Fail Fast, Consolidate Faster

    Modern organizations have become very good at trying new technology and much less disciplined at absorbing it. In the AI era, the risk is not experimentation itself, but experimentation without consolidation.

  4. AI Engineering Needs Audit, Not Faith

    Useful AI in engineering keeps the same property useful tests have: a human can read what happened, where it went wrong, and decide what to do next. The argument is not that AI is dangerous. It is that anything shipping to production has to be inspectable when it fails.

Notes

  1. Evals Are Tests

    The reluctance to call AI evaluation harnesses tests costs more than the renaming saves. Tests get reviewed, run in CI, and have an owner. Evals deserve the same treatment.