AI decisions, examined in depth
Longer takes on the topics that matter for AI leadership. Each article builds on a LinkedIn post with added context, evidence, and frameworks.
The Check That Left With the Afternoon
A McKinsey case describes a distributor whose system reads public building permits and drafts the sales outreach. The work it removed was somebody's afternoon, and an afternoon is never only one job: while a person was assembling the material, they were also reading it. That reading was a control nobody had written down, and it leaves with the work.
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Don't Build What You Can't Keep
McKinsey's 2026 survey found 32% of respondents saying their organization decided against buying at least one software product or feature because it could be built with coding agents. The companion post argued that the buy rule grew a second half. This article does the three things the post could not: reads the survey's size gap honestly, sorts builds into the three kinds that need different rules, and shows how to price what keeping a built tool costs every month.
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Cheap to Write, Expensive to Keep
A system that remembers across more than one layer has a stratum you can delete with no consequence and a stratum that takes everything with it. Cost asymmetry moves important knowledge into the cheap one without anyone deciding it. This is the evidence for why that happens, and why better writing tools do not fix it.
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What Stays Expensive
The peer-reviewed version of a landmark AI productivity study found the gains landed on the least skilled and quality slipped slightly for the most skilled. That is not a productivity result. It is a repricing of what you have been paying for.
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Assessment or Echo
An agent's recommendation is assembled out of what it wrote down and how you asked. Neither of those is visible in the recommendation itself.
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An Agent Is Not an App
Software waits. An agent acts. That single difference is why agent projects run on a hiring clock, and why the invoice is the wrong document to plan from.
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