Essays
Original perspectives on the decisions behind operational AI—what to fund, how work should change, what production demands, and how organizations build lasting capability.
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AI value shows up when the workflow changes. A faster draft is useful, but the bigger upside is when context, review, approval, and system updates move together.
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A data moat is not a pile of records. For AI agents, the moat is the context the workflow can retrieve, trust, explain, and improve over time.
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Tokens are an input metric. Enterprise AI value shows up when a workflow produces trusted, accepted, economically useful work.
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The AI talent gap is not only a shortage of AI engineers. It is a shortage of people who can operate agents inside real workflows after launch.
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Model routing can lower cost and improve fit, but it does not replace the operating layer. Context, evals, permissions, audit, and write-backs should stay horizontal.
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The second agent proves whether the first one was a pilot or reusable infrastructure. Expansion should compound context, controls, monitoring, and ownership.
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