Compare AI ecosystems without hiding which data and weights produce the ranking.
Choose the signals you want to compare, change their weights, and see which countries remain comparable. Missing observations exclude a country from that scenario: they are never converted to zero or silently compensated for.
Relative ranking with visible coverage
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The index is a configurable view, not an absolute measure of who is “winning AI.”
Investment, companies, data centers and models use log(1+x) before min–max scaling so one extreme value does not compress the rest of the range. Talent and policy use a linear scale.
A country enters the ranking only when every active signal is available. Changing signals can change both the country set and the normalized values, so each scenario is relative to its own coverage.
Active weights are normalized to 100%. Equal weighting is the default. There are no hidden “expert” weights and no fixed editorial 5sigmas score.
28 records do not mean 28 countries are comparable in every scenario.
The universe is the union of countries published in at least one selected source. AI Index 2026 shows 15 areas in each of the investment, company-formation and data-center figures, but they are not the same 15. Coverage is preserved exactly as published: absence from a figure is missing data, never zero.
The initial scenario has 12 comparable countries because it requires both 2025 private investment and newly funded AI companies. Adding data centers leaves 9. That reduction is information about dataset coverage, not a defect hidden by imputation.
Observable signals with explicit limitations.
Private investment does not necessarily include state spending. Data-center counts do not measure facility size or available GPUs. Notable-model counts depend on Epoch AI's criteria and the 2026 figure publishes only selected geographies. LinkedIn profiles are not a population census. Oxford Insights Policy Capacity is one methodological pillar, not a complete measure of government execution.
Stanford's 2024 Global AI Vibrancy score is shown only as an external reference where a value has already been verified and never enters the 5sigmas scenario score, avoiding double-counting an already-composite index as if it were a primary observation.
Every signal keeps its organization, period and interpretation limit.
Inspired by AI Index transparency, not by its score.
Stanford's Global AI Vibrancy Tool compares 36 countries using 23 indicators across 7 pillars and lets users adjust weights. 5sigmas adopts the reproducible-exploration idea while using its own signals, transformations and coverage rules. The goal is to make every ranking change explainable from the data and weights.