- Essays··7 min read
The 73% You Never See
Riverside County's AI form-filling tool delivered a 73% cut in application processing time — a clean institutional metric that says nothing about whether applicants left with every programme they qualified for. When a county measures throughput and error rate rather than referral breadth or benefit value per case, the AI ships the efficiency, the institution keeps it, and the resident encounters an already-incomplete system running faster.
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The '19%' problem: what Gartner's productivity surveys actually measured this spring
Two Gartner surveys published in May 2026 reveal a measurement crisis enterprises have not yet named: time saved per AI seat is real, but organisations lack the controls to redeploy that capacity or quality-gate its output before it reaches the P&L. Between 60% and 89% of firms see zero detectable productivity impact despite widespread adoption. That is a governance problem that registers as a measurement problem and gets blamed on the models.
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the productivity number nobody can actually measure
When the number is real, the baseline comes with it. Everything else is a story about the future wearing a percentage sign.
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the 0.1% problem · when the productivity number depends on who's holding the ruler
BofA's 10x and the economy's 0.1% measure different things and call both 'productivity'. The number is only as honest as the ruler, and right now almost everyone is holding the ruler that pays them.
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What Purdue Required, Estonia Already Built
America is writing AI-competency requirements into degree audits without a defensible idea of what competent looks like. Estonia attached the measurement. The press release is not the part that will hold up in 2030.
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Sunday Essay · The Three Productivity Curves We Refuse to Reconcile
Three credible measurement traditions, applied to the same workforce in 2026, point in three different directions. The discourse treats them as one number. The gaps between them are the actual managerial signal.
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