- Essays··8 min read
Nadella's Half-Drawn Ledger
Satya Nadella's essay on the reverse information paradox names a real problem: enterprise buyers surrender proprietary knowledge — prompts, corrections, evaluation harnesses — to AI vendors with each query. The framing is analytically sound. It is also written by the CEO most commercially exposed to a future in which that knowledge lives outside his cloud tenant. The enterprise buyer's job is to notice which parts of the argument survive when the messenger changes.
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Uber Burned Its Entire 2026 AI Budget in Four Months — Here's the Bill Coming Due
Uber exhausted its entire 2026 AI coding budget by April — not an overshoot, a full burn. Beneath that headline sits a three-layer debt stack: technical debt, ungoverned prompt sprawl, and cost overhang accrued during a subsidised-inference era now ending. Organisations that treat AI spend as an architectural concern from day one will survive what follows; those that audit costs only after the invoice arrives will not.
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Mandates without mastery: the AI upskilling gap that vendor dashboards hide
Corporate AI mandates — Google tying usage to performance reviews, Meta tracking AI-assisted output, Microsoft declaring it non-optional — now outrun demonstrable capability. Eighty-two per cent of organisations provide AI training; 59% still report a skills gap. The deficit is not access — completion certificates measure the wrong thing, and firms are applying one-time training models to a skill that demands continuous, deliberate practice on realistic tasks.
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The Slop Debt Bill Is Due, and Nobody on the Org Chart Owns It
Slop debt is the space between what you pay for AI and what you do not get. The bill is in the inbox; the question is whether the org chart catches up before the auditor does.
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the kernel of truth and the trillion-dollar bet
A bubble is not the same thing as a lie. There is a real technology, it gets oversold, and the financing built on the overselling becomes the actual fragility. The right question isn't whether AI is a bubble — it's who's holding the debt when demand disappoints.
ai-bubblehyperscaler-capexcircular-financingproductivity-paradoxRead - Essays··7 min read
Two Hundred Million to Study the Wave You're Making
A frontier lab has priced in the scenario where its product makes office work unnecessary — and is funding the academics who'll help governments respond. Adoption is wide; realisation is narrow. The gap is now numerical.
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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% aren't in contradiction — they 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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The bill nobody booked
The most expensive line item in your AI budget for the next two years is the one your finance team has not yet named. It is sitting in your environment already — half-built pilots, ghost fine-tunes, redundant copilots — capitalising itself into your monthly cloud invoice.
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13%: the upskilling number that quietly breaks the agentic AI business case
Only 13% of employees can actually work with the agents their firms are deploying. The centaur the whole thesis assumes is mostly a novice holding the reins — and AI is demolishing the training ground that produced the experts.
agentic-aiworkforce-upskillingdeliberate-practiceapprenticeshipRead - Essays··11 min read
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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The owners are right. That's the part nobody wants to sit with.
The owners are directionally right that they can run with far fewer people. They are very often wrong about which people, how fast, and what it costs to get there. Those two things are the whole story.
ai-and-labourworkforce-displacemententerprise-aiautomation-economicsRead - Essays··7 min read
Twelve Percent Faster, and Other Numbers I Don't Yet Believe
Four data sets, three methodologies, and no one who matters can yet give you a single defensible figure for AI's effect on knowledge-work productivity. The measurement culture is not up to the tooling — and the vendors will close that gap last.
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