Category: Risk & Decisions
Practical frameworks for making decisions under uncertainty, evaluating risk, checking evidence, and understanding where models and metrics can mislead.
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The AI War Is No Longer About Better Answers. It’s About Getting Permission
OpenAI and Google are no longer competing only on model quality. Dots, Gemini Connected Apps and new agent…
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AI Has Started Learning From AI
The fight over Claude distillation looks like another U.S.–China technology dispute. Underneath it is something stranger: AI systems…
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The AI That Predicts Your Future Will Have a Problem: You Can Read the Prediction
An AI could know us statistically well enough to anticipate our decisions and probable futures. The problem begins…
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Seismic Risk in the AI Era: What Has Actually Improved (and What Hasn’t)
AI improved the speed of earthquake early warning and parametric insurance, but did not solve earthquake prediction. What…
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CIES Nearly Nailed Neymar’s €222M Price. The Lesson Is Better Than “Algorithm vs. Intuition”
PSG paid €222 million for Neymar in 2017. Contrary to this article’s previous claim, CIES did not value…
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AI Sports Predictions: How to Tell Whether an Accuracy Number Means Anything
An 80% or 90% hit rate can sound impressive and still tell you very little. Evaluate the baseline,…
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What We Mean by “Scalar Valuation” When Comparing Tools
ScalarPivot uses “scalar valuation” as shorthand for making weights explicit and reducing several criteria to a comparable score.…
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How to Evaluate the Risk of Automating a Decision With AI
Before turning an AI workflow into a score, separate impact, reversibility, scale, and legal requirements. A score can…
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AI Reputation Risk: When Should You Automate?
A practical four-factor framework for deciding when AI drafting is useful, when verification becomes the real cost, and…
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How to Make Better Tool and Workflow Decisions Under Incomplete Information
How to make high-stakes tool and workflow decisions under incomplete information using structured decision matrices.