Methodology & Validation

01 · the exact measurement

tool registry, payload groups & anchor normalization

Google Trends compares at most 5 terms per payload, so the 13 tools are pulled in three payload groups with Gemini AI as the shared anchor term in every group. share of Google search interest — relative preference, not usage counts.

for each payload group G in {B, C}, per country r:

  scale_G(r) = A[r]["Gemini AI"] / G[r]["Gemini AI"]     when both anchor readings ≥ 2
             = mean of that ratio over all countries          otherwise (group-mean ratio;
               where both anchors ≥ 2                          1.0 if no such country)

  value[r][tool] = raw_G[r][term] × scale_G(r)      (duplicate anchor columns dropped;
                                                     gemini's value comes from group A only)

  share[r][tool] = value[r][tool] / Σ value[r][·] × 100     → the 13 tools sum to ~100% per country

time series: identical logic PER TIMEPOINT (anchor ≥ 2 test, else the series-wide mean ratio).

02 · the five signal families

every endpoint, exactly as queried

all five families are fetched by the same daily job and archived as raw snapshots under /history/ — see the research console for the full data dictionary and API.

03 · known limitations

what this data cannot tell you

the caveats that belong in any methods section built on this dataset — stated here once, plainly.

• Trends sampling & rescaling opacity. Google Trends values are a sampled, 0–100-quantized estimate; the same query can return slightly different numbers on different days and Google does not document the sampling. The shared anchor and daily pulls dampen this, but day-to-day jitter of a point or two is expected and real.
• Relative, not absolute. A 40% share says nothing about how many people searched — only what a country's AI-engaged searchers preferred among the 13 tracked tools. Search interest ≠ usage.
• 1-week windows. Country shares come from a single rolling week (“now 7-d”), so news-cycle spikes move them; the Anthropic AEI benchmark used in validation is likewise built from ~1-week Claude.ai usage windows. Both are snapshots, not long-run averages.
• Low-volume countries excluded. inc_low_vol=False means only ~84 countries clear Trends' volume floor; countries absent from the map are unmeasured, not zero.
• Anchor threshold. Cross-group scaling trusts the per-country anchor only when both readings are ≥ 2; below that a group-mean ratio substitutes, so group-B/C values in small countries are noisier than group-A values.
• CI-inflated downloads. npm and PyPI weekly downloads count CI machines, mirrors and bots — momentum, not headcount. PyPI is pulled with mirrors=false, which removes only known mirrors.
• Redirect-era Wikipedia attribution. Wikimedia attributes pageviews before an article rename to the old title, so early segments of renamed articles (e.g. “Claude (AI)” before ~mid-2026) understate attention. Titles are redirect-resolved at fetch time since v1.1.
• Term ambiguity. Search terms like “Grok” or “Cursor AI” can capture unrelated interest; terms are frozen in the registry above and only change with a changelog entry.
• Stars are cumulative. GitHub star counts are lifetime totals — day-over-day deltas are the momentum signal; stars ≠ users.
• The archive starts 2026-07-07. Country-level daily history begins at the archive start and is not retroactively reconstructible from Google Trends.

04 · validation study

construct validity — how our numbers compare to vendor ground truth

Spearman rank correlation (primary; robust, valid for ordinal data) and Pearson on log-transformed values where both measures are continuous. Country names/ISO codes joined via explicit alias table. Pure-python; script: scripts/validation_study.py. Stanford HAI 2026 excluded: its country genAI figures republish Microsoft's (not independent).

validation computed 2026-07-08 · the validation script (validation_study.py) and the full data dictionary ship inside the Zenodo deposit README, so every figure above is reproducible from the archive.

05 · methodology changelog

every change, on the record

this changelog is append-only: every change that touches how a number is computed gets an entry before it ships, and any change that breaks comparability of a series with its own past is flagged as a series break with its exact scope. comparing values across time? check the breaks first. newest first.

06 · how to cite

put it in the bibliography

DOI: 10.5281/zenodo.21252715 (concept DOI 10.5281/zenodo.21252714).

PIXIPACE. (2026). Who is using AI? — Global AI Tracker dataset [Data set]. whoisusingai.com. Retrieved Jul 8, 2026, from https://whoisusingai.com/tracker/research https://doi.org/10.5281/zenodo.21252715

@misc{whoisusingai2026tracker,
  author       = {{PIXIPACE}},
  title        = {Who is using AI? --- Global AI Tracker dataset},
  year         = {2026},
  url          = {https://whoisusingai.com/tracker/research},
  doi          = {10.5281/zenodo.21252715},
  note         = {Accessed 2026-07-08}
}

the data and the plain-JSON API live on the research console. the collection and build scripts are not published on this site — the validation script and the full data dictionary ship inside the Zenodo deposit README instead. search shares are relative Google search interest, not usage counts — say so in your methods section.