The lead-gen middlemen sitting between buyers and Hawaii wealth firms.

42-firm cohort, 18 hash-locked questions, 3 usable runs across 2026-05-23 to 2026-05-25. Pattern-readiness cleared. Individual firms anonymized; counts, distributions, and named third-party platforms named.

Why this category matters as a measurement subject

Hawaii wealth management is the relationship-end category in our cross-category teardown. A buyer choosing a wealth manager makes a high-trust, long-horizon decision based on the firm's principals, philosophy, and fit with the buyer's specific scenario (business owner, physician, inherited wealth, etc.). The category's AI-citation surface reflects that: relatively low firm-owned share (38%) and high third-party share (59%) compared to commodity categories like banking. The third-party surface is where the closable competitive ground sits.

Methodology summary

Same 7-AI-tool methodology applied across all NeverRanked teardowns:

• 5 web-searching AI tools: Perplexity (sonar API), ChatGPT search (gpt-4o-mini-search-preview), Gemini grounded (gemini-2.5-flash with Google Search), Microsoft Copilot via Bing organic results, Google AI Overviews.
• 2 training-data AI tools: Claude (claude-haiku-4-5, via Anthropic API), Gemma (open-weight, via Together AI).

18 questions a Hawaii wealth-management buyer would actually ask AI, locked at hash ae13579a... so every run compares apples to apples. The actual locked set is published at /methodology/#query-set for review. 3 repetitions per question per AI tool. 3 usable runs spanning 2026-05-23 to 2026-05-25. Pattern-readiness rule of 3 usable runs cleared per MOAT.md rule 5.

The 42-firm cohort grew through two passes. 15 anchor firms registered before run 1. 8 additional firms surfaced through run 1 cohort-coverage scan and registered after. The cohort expansion lifted the firm-owned share from 27% on run 1 to 38% on run 3, illustrating the cohort-coverage discipline named on the methodology page. National lead-gen platforms and financial-planner directories were deliberately excluded from the cohort; they appear in the data as third-party content sources.

Full methodology + open-source measurement code at /methodology/.

Source-type distribution (cohort-wide)

Across all 23 firms and all 7 AI tools, AI pulled answers from these source types:

Hawaii wealth management sits in the middle of the cross-category cluster. For comparison: Hawaii consumer banking is 53% firm-owned (uniquely high), Honolulu dental is 44%, and Hawaii law firms is 39%. The earlier reading on this measurement (1 run, 15 firms) showed 27% firm-owned and put wealth in the "relationship-end" tier with law. Three rounds of cohort expansion (15 firms grew to 42) showed that reading was an artifact of incomplete cohort coverage. The real number sits in the cluster with dental and law. Banking is the outlier. Why banking is different is the more interesting question, and we have a hypothesis on the cross-category teardown.

The lead-gen middleman pattern

The 3,029 third-party-content mentions are not all the same shape. The top recurring non-firm sources across runs:

The structural finding is in the top two rows. SmartAsset (211 mentions) and getwarmer.com (221 mentions) are both lead-gen platforms whose business model is collecting buyer information and routing it to wealth advisors who pay for placement. Combined, they account for 432 of the 3,029 third-party mentions (14% of all non-firm citations). AI is using these platforms as primary sources when answering wealth-management questions for Hawaii.

For a wealth-management firm, this is a real structural fact about the category. If your firm is not represented on SmartAsset and getwarmer.com, you are missing a meaningful share of the AI-citation surface, even before considering Microsoft Copilot or any of the per-firm dynamics. This is also a different shape of surface than the categories below: dental does not have a SmartAsset equivalent; banking does not have a getwarmer.com. Wealth has both because the lead-gen advisor-matchmaker model is a real economic layer in this vertical.

Per-AI-tool breakdown, the cohort-wide Copilot gap

Two rows worth flagging. Microsoft Copilot cites zero firm-owned websites across 787 mentions, the same cohort-wide gap we documented for dental and law. Whichever Hawaii wealth firm ranks first in Bing organic results for the buyer questions effectively owns the Copilot answer while every competitor is invisible there.

The Claude row is the more surprising finding. Claude answers from training data and cites firm-owned websites only 14% of the time, by far the lowest of any AI tool in this cohort. The other 86% of Claude's mentions pull from third-party content. For a wealth firm asking "does Claude know my brand," the answer is largely "Claude knows the category but reaches for third-party context before naming any specific firm." Compare this to law firms where Claude cites firms at 51% (training-data presence for the named firms is strong) or to dental where Claude cites practices at 34% (training-data presence is moderate). Hawaii wealth firms have a notably weaker presence in Claude's training data than the other relationship-business cohort we measured.

Top recurring firms (anonymized)

The 5 firms AI cited most often across the 18 questions and 7 tools:

The top 5 firms account for 36% of all firm-owned mentions (1,062 of 2,944). This is a notably less concentrated category than Hawaii law firms, where the top 5 owned 66% of competitor mentions. Wealth management has a flatter distribution: the long tail of firms has more meaningful share, and the relative gap between the top firm and the fifth firm is smaller. For a firm outside the top 5, the gap to closing is shorter in wealth than in law.

What this teardown does and does not prove

What it does support:

• The 42-firm cohort is a representative slice of Hawaii wealth-management firms AI tools actually cite for buyer-shaped questions.
• The 38%/59% firm-owned / third-party split is stable: 37% on run 2, 38% on run 3 (same cohort). Run-to-run noise is not driving the headline.
• The Microsoft Copilot 0% firm-owned share is cohort-wide and consistent across all 3 measurement runs.
• SmartAsset and getwarmer.com are measurable AEO surfaces specific to this category that most Hawaii wealth firms likely do not actively manage.
• Claude's training-data presence for Hawaii wealth firms is structurally thinner than for adjacent relationship categories.

What it does not yet support:

• That presence on SmartAsset or getwarmer.com would cause AI to cite a firm differently. We measured what AI cites. Causation requires pre-registered experiments. Different scope.
• That Claude's thin Hawaii-wealth training-data presence will close on any specific timeline. Training-data presence is the slowest signal we measure; it forms over years of editorial and brand authority work.
• That the Microsoft Copilot first-mover opportunity is actually closable for any specific firm. The Bing organic ranking competition is outside what this measurement captures.

Why this is anonymized

None of the 23 firms in this cohort are paying NeverRanked customers. The non-customer anonymization rule applies: counts, distributions, source-type breakdowns, and per-AI-tool numbers are public; individual firm names are not. The pattern is what is informative on a public surface. The named cohort lives only inside paid engagement deliverables, where the named firm is the customer authorizing the use.

One related private artifact exists: a 1:1 brief prepared for Hamada Financial Group as part of an in-person scoping conversation. That brief names the cohort in full and is private to Hamada (noindex, nofollow, takedown on request). The public teardown you are reading is the anonymized version of the same underlying measurement.

A firm that becomes a NeverRanked customer gets a 1:1 deliverable that names every firm in the cohort, names the queries the customer is missing on, and ranks the closable conditions. That deliverable is private to the customer.