Large language models increasingly: • frame technical problems • suggest tools and vendors • compress competitors into substitutes • decide who belongs in a category — silently
This happens before: • SEO • paid media • analytics • pipeline attribution
These rankings can’t be inferred from analytics, SEO tools, or internal dashboards—because they exist upstream of clicks. They describe the categories AI uses to answer customer questions before discovery begins.
In several bins AI uses to place Texas Instruments, the default “owner” in answers is not always Texas Instruments: • Voltage Regulators: Analog Devices is #1 by node signal; Texas Instruments ranks —• MCUs: Analog Devices is #1 by node signal; Texas Instruments ranks —• ADCs: Analog Devices is #1 by node signal; Texas Instruments ranks — This is not a branding claim — it’s a measurement of what AI defaults to before a click happens.
Display ICs — score 3.4983 — low resistance; KIM: integrated-circuits-ics • Highest ranked, lowest resistance • Clear “default” doorway to expand from
Memory and Storage — opp 10.81 / res 3.22 — KIM: Unmapped (KIM) • High upside, requires sustained ownership • More content + assets + integration signals
“Open” is a content + asset doorway: a targeted set of pages and artifacts that cause models to reliably associate Texas Instruments with a category phrase (and surrounding workflow questions). Think: reference designs, app notes, BOM-ready examples, selection tools, and comparison pages that match customer phrasing.
Weekly movement in the same phrases across model families. Doors “open” when Texas Instruments appears more often in high-consensus answers and when competitor dominance shrinks in the same nodes.
Suggested next step: a 30–45 minute working session to review the ranked lists, validate the top 5 doors, and identify 2–3 doors to pilot (one fast win + one long bet).
A ranked action plan tied to how AI answers customer questions—plus a weekly measurement loop that shows perception shifts before they show up in pipeline.