How AI Search Is Recommending Roth IRAs

Investors shopping for a Roth IRA provider are no longer relying solely on Google searches and brand websites. They are asking AI systems to compare brokerage platforms, explain fee structures, surface alternatives, and recommend shortlists. The buyer shortlist is now being formed inside AI-generated responses, and the competitive dynamics of the category are shifting as a result.

The LLM Authority Index benchmark for Roth IRAs reveals a clear hierarchy forming across six AI platforms. Charles Schwab dominates recommendation-stage visibility with a 51.3% top-three rate and 55.3% valid recommendation coverage across 1,384 observations. Fidelity and Vanguard occupy the next tier, while several well-known brands appear frequently in AI responses but rarely earn top shortlist positions. CiteWorks Studio interprets this benchmark data to show which brands are winning AI recommendations, which are visible but not selected, and what the gap means for market positioning.

Methodology

1. Market studied: Roth IRA providers and brokerage platforms offering Roth IRA accounts.
2. Brands/entities included: Charles Schwab, Fidelity, Vanguard, Robinhood, Betterment, Wealthfront, SoFi, E*TRADE, M1 Finance, and Merrill Edge. This is not a complete market census.
3. Data collection date/window: June 2026, snapshot-based.
4. AI platforms tested: ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, and Perplexity.
5. Number of prompts tested: Prompt count was not provided. A total of 1,384 observations were analyzed across three buyer-stage clusters.
6. Prompt categories: Discovery (awareness-stage), Comparison (consideration-stage), and Pricing and Fees (decision-stage).
7. Definition of a mention: A mention means the company appeared in an AI-generated response, regardless of sentiment or rank.
8. Definition of a valid recommendation: A valid recommendation is a positive, shortlist-quality recommendation or ranked recommendation that earns recommendation credit. Visibility is not the same as recommendation credit. A brand can be named in an AI response without being advanced as a shortlist choice.
9. Ranking/scoring metrics used: Valid recommendation coverage, top-three rate, rank-one rate, top-ten rate, average rank, net sentiment score, and modeled monthly AI Authority Value, which includes AI Recommendation Value and AI Visibility Assist Value components.
10. Limitations: This is a point-in-time benchmark. AI outputs can change. Modeled values are estimates based on commercial intent proxies and are not revenue, pipeline, or booked sales. This report is not a full audit or complete market census. Prompt count was not provided and observation-level analysis was used in its place.

Key Findings

Charles Schwab leads the category across nearly every recommendation metric. The benchmark shows Charles Schwab appearing in 72.6% of all AI responses and earning a valid recommendation in 55.3% of observations. Its top-three rate of 51.3% is the highest in the study. Its average rank of 2.03 means it consistently appears near the top of AI-generated shortlists. The analysis found Charles Schwab captures an estimated $1.86M in modeled monthly AI Authority Value, more than any competitor measured in the study.

Fidelity is the strongest first-choice recommendation when selected. The dataset marks Fidelity with an average rank of 1.38, the best in the category. Its rank-one rate of 23.5% is more than double that of most competitors. Fidelity earns a valid recommendation in 31.1% of observations across all platforms and captures an estimated $1.07M in modeled monthly AI Authority Value. The evidence suggests that when AI systems select Fidelity, they tend to place it first.

Robinhood, Betterment, and Wealthfront show a significant gap between visibility and recommendation power. Robinhood appears in 54.1% of all AI responses but earns a top-three recommendation only 15.8% of the time. Betterment appears in 42.1% of responses with a top-three rate of 7.4%. Wealthfront appears in 37.3% of responses with a top-three rate of 5.9%. These brands are recognized by AI systems but are not consistently advanced as top choices, which means their presence is not translating into shortlist credit.

Merrill Edge presents the category's most visible framing risk. The benchmark shows Merrill Edge appearing in only 9.4% of AI responses, the lowest presence rate among all measured brands. It earns a valid recommendation in just 3.5% of observations. Its net sentiment score of 0.44 is the lowest in the category, and the dataset records negative sentiment in 0.7% of its observations. This pattern reflects a framing problem, not merely a visibility problem.

The Pricing and Fees cluster carries the highest commercial value and is dominated by Charles Schwab. This decision-stage cluster generated 471 observations with a modeled opportunity of $18.1M. Charles Schwab leads with a 51.2% top-three rate in this cluster, followed by Vanguard at 34.8% and Fidelity at 32.7%. Buyers in this cluster are making final decisions based on cost and fee structures, and top recommendation placement carries the highest commercial weight among the three clusters studied.

What Changed in the Market

Investors researching Roth IRA providers are no longer moving only from Google search results to brand websites. They are asking AI systems to compare providers, explain fee structures, surface alternatives, and recommend shortlists. This shift changes where the buyer shortlist is formed and which brands benefit from recommendation-stage visibility at the decision moment.

For a trust-heavy category like Roth IRAs, the shift carries particular weight. Investors are making decisions about long-term retirement savings, and they rely on AI systems to surface providers that are legitimate, cost-effective, and well-reviewed. The brands that win AI recommendations are not necessarily the cheapest or the most innovative. They are the brands with the most citable, structured, and consistent evidence across the source types that AI systems retrieve and synthesize.

The result is shortlist compression. AI platforms are concentrating recommendations around a small set of providers. Charles Schwab, Fidelity, and Vanguard are the primary beneficiaries of this pattern. Every other brand in the study faces some version of a recommendation gap, and several face a severe version of that gap, where high presence in AI responses does not translate into meaningful shortlist placement.

The buyer journey now includes a stage that did not exist at scale three years ago. A prospective investor asks an AI system which Roth IRA providers to consider. The AI system generates a ranked shortlist. The brands on that shortlist receive the first call, the first comparison, and in many cases the final decision. Brands not on the shortlist are invisible at the moment that matters most, regardless of how visible they are in traditional search.

What the Benchmark Found

Recommendation leaders

Charles Schwab is the recommendation leader in the Roth IRA category. It leads in valid recommendation coverage at 55.3%, top-three rate at 51.3%, and modeled monthly AI Authority Value at $1.86M. It also leads across all three buyer-stage clusters. On Google AI Overviews, the analysis found Charles Schwab achieving a top-three rate above 60%, suggesting particularly strong structured evidence within Google's own synthesis layer.

Fidelity is the strongest first-choice recommendation when selected. Its average rank of 1.38 is the best in the category, and its rank-one rate of 23.5% is more than double most competitors. Fidelity performs particularly well on Copilot and Perplexity, where the benchmark shows top-three rates above 52% and 56% respectively. Fidelity captures an estimated $1.07M in modeled monthly AI Authority Value.

Strong recommendation with lower presence

Vanguard earns a 39.9% valid recommendation coverage rate and a 32.2% top-three rate despite appearing in fewer Discovery-stage prompts than Robinhood. Its presence rate of 58.4% is the second-highest in the category, and its average rank of 3.01 reflects consistent shortlist inclusion, though not top positioning. Vanguard captures an estimated $782K in modeled monthly AI Authority Value. The source pattern may indicate that Vanguard's evidence layer is strongest in comparison and fee-focused content.

Visible but under-recommended

Robinhood appears in 54.1% of AI responses, nearly matching Vanguard in presence. However, its top-three rate of 15.8% is roughly half of Vanguard's, and its average rank of 3.74 indicates it tends to appear lower in shortlists. Robinhood earns a valid recommendation in 38.6% of observations but captures only an estimated $612K in modeled monthly AI Authority Value, reflecting the commercial weight penalty that comes with lower average rank.

Betterment appears in 42.1% of AI responses and earns a valid recommendation in 29.3% of observations. Its top-three rate of 7.4% is low relative to its presence. Betterment captures an estimated $927K in modeled monthly AI Authority Value, driven partly by strong visibility assist value on ChatGPT and Gemini, where it appears as a supporting reference even when not advancing to shortlist positions.

Wealthfront appears in 37.3% of AI responses and earns a valid recommendation in 25.1% of observations. Its top-three rate of 5.9% is among the lowest in the category. Its average rank of 4.18 reflects consistent mid-list placement. Wealthfront's presence is stronger in the Discovery cluster than in the Pricing and Fees cluster, suggesting the source footprint is thinner at the decision stage.

SoFi appears in the dataset but its metrics across all clusters reflect limited recommendation-stage presence. Its valid recommendation coverage and top-three rate are below the category median, and its modeled AI Authority Value is modest. The analysis found SoFi performing best on platforms where Roth IRA content is evaluated alongside broader financial planning topics.

Present but commercially weak

ETRADE appears in 17.3% of AI responses but earns a valid recommendation in only 7.5% of observations. Its top-three rate is 1.8%, and the dataset records zero rank-one recommendations on ChatGPT, Gemini, and Google AI Mode. Its net sentiment score of 0.57 is the second-lowest in the category. ETRADE is visible enough to confirm that AI systems have indexed its brand, but the evidence suggests the framing and source footprint are not generating shortlist-quality outcomes.

M1 Finance appears in 14.1% of AI responses and earns a valid recommendation in 7.9% of observations. Its top-three rate of 3.0% is low overall, though the benchmark shows stronger performance on Perplexity, where it achieves a 9.4% rank-one rate. This platform-specific pattern may indicate that Perplexity's source retrieval layer is picking up content that other platforms are not weighting as heavily.

Merrill Edge has the weakest AI recommendation position in the category. It appears in only 9.4% of AI responses, earns a valid recommendation in 3.5% of observations, carries a net sentiment score of 0.44, and records negative sentiment in 0.7% of observations. On Google AI Mode, the benchmark shows Merrill Edge appearing in 15.8% of responses but earning a valid recommendation in only 2.5% of those appearances. The gap between presence and recommendation credit is the widest in the category.

Note: Several cells show "Not provided" because full per-brand metric tables were not included in the available dataset. Values shown reflect the metrics that were explicitly reported. Modeled monthly AI Authority Value figures are estimates and are not revenue.

Why Visibility Is Not Enough

A brand can appear in AI answers and still fail to win the buyer shortlist. This is the central commercial distinction in the Roth IRA category, and the benchmark makes it visible.

Raw mention presence measures how often a company is named in an AI-generated response. Valid recommendation coverage measures how often a company is actually recommended or shortlisted with positive, shortlist-quality framing. These are not the same signal, and the Roth IRA dataset illustrates the gap clearly. Robinhood appears in 54.1% of AI responses but earns a top-three recommendation only 15.8% of the time. Betterment appears in 42.1% of responses but earns a top-three recommendation only 7.4% of the time. Presence without shortlist placement does not move the buyer.

Top-three placement and rank-one placement carry different commercial weights. Being named in a five-brand list is not the same as being the first recommendation. Fidelity's average rank of 1.38 means it is usually placed first when selected, which gives it outsized influence at the decision moment. A brand with a 40% mention rate and an average rank of 4.5 is not commercially equivalent to a brand with a 40% mention rate and an average rank of 1.5, even if their raw presence numbers look similar.

Neutral or cautionary mentions do not earn recommendation credit. When AI systems flag concerns about fees, regulatory history, investment complexity, or account minimums without a clear endorsement, the mention is present but the commercial value is weak or negative. Merrill Edge's net sentiment score of 0.44 and its 0.7% negative sentiment rate reflect a pattern where presence in AI responses is not translating into buyer confidence.

Citation frequency is not the same as endorsement. AI systems may cite a brand's website, regulatory filing, or news article without recommending it. A brand that appears in a comparison article as a lower-ranked alternative is cited but not advanced. The difference matters for how brands should interpret their AI visibility reports.

Modeled AI Authority Value is not revenue. It is a directional estimate of the commercial weight of recommendation-stage visibility, based on intent proxies, average rank, and shortlist-quality framing. It is a benchmark signal, not a sales forecast. The $18.1M modeled opportunity in the Pricing and Fees cluster represents the commercial weight of the recommendation decisions being made in that prompt cluster, not guaranteed revenue for any single brand.

The Citation Layer

AI systems do not recommend Roth IRA providers randomly. They retrieve evidence from public sources, compare options, and synthesize ranked responses based on the strength and consistency of available information. The source types that appear to shape Roth IRA recommendations include official brand content, fee disclosure pages, editorial comparison articles, review platforms, personal finance community discussions, and regulatory or institutional references.

Charles Schwab, Fidelity, and Vanguard appear to benefit from deep citation coverage across these source types. Their fee structures are clearly documented and widely referenced. They appear in comparison and editorial content that AI systems treat as authoritative and easy to retrieve. They have strong organic search footprints with ranking pages across high-intent Roth IRA keywords. This combination creates a stronger source footprint, which gives AI systems more accurate and consistent material to synthesize into a shortlist recommendation.

Brands with weaker recommendation coverage may be missing one or more of these evidence layers. They may appear in factual brand references but not in comparison or evaluation content that earns shortlist credit. A brand that is well-known to consumers but has thin structured coverage in third-party review and comparison sources will be visible to AI systems without being consistently recommended by them.

Forum and community discussions also contribute to the source pattern in this category. Personal finance communities, Reddit threads on retirement investing, and user-generated comparison discussions are retrievable by AI systems and may influence how brands are framed in responses, particularly in Discovery-stage and Comparison-stage prompts. Brands that are discussed positively in these communities, with consistent factual claims about fees, account features, and investment options, may benefit from the density of community-sourced evidence.

Traditional search visibility is part of the public evidence layer. Brands with strong organic search footprints, high-authority referring domains, and ranking pages across fee comparison, account minimum, and Roth IRA review keywords create more retrievable material for AI systems. This is supporting evidence for the broader citation architecture, not a direct cause of AI recommendation outcomes. The source footprint must be citable, structured, and framed positively to support shortlist-quality recommendations.

What Brands Need to Fix

Weak valid recommendation coverage. E*TRADE, M1 Finance, and Merrill Edge appear in AI responses at measurable rates but earn valid recommendations in a small fraction of observations. The gap between mention presence and recommendation coverage in these cases is wide enough to suggest that the brands are recognized but not trusted at the shortlist stage. Closing this gap requires improving the quality and structure of the evidence that AI systems retrieve, not merely increasing brand mentions.

Low top-three and rank-one presence. Robinhood, Betterment, and Wealthfront appear frequently but rarely earn top-three placement. Improving top-three performance requires stronger evidence in comparison and evaluation-stage content, particularly content that positions the brand against competitors in a way that AI systems can retrieve and rank.

Poor prompt-cluster coverage. Some brands perform better in Discovery prompts than in Pricing and Fees prompts. This creates gaps in the buyer journey where competitors can intercept demand at higher-intent stages. Brands with strong Discovery presence but weak decision-stage recommendation rates are winning awareness but losing the shortlist.

Neutral or cautionary framing. Merrill Edge carries the lowest net sentiment score in the category. Brands with neutral or cautionary framing in AI responses need to improve the quality of their public evidence layer, not just its volume. The sources that AI systems retrieve must support positive, shortlist-quality framing.

Thin source footprint. Brands with low recommendation coverage may lack sufficient citable content across comparison, review, fee documentation, and trust signal source types. Building the source footprint means identifying which content categories are missing and creating or earning coverage in those areas.

Inconsistent entity information. Brands that are named inconsistently across sources, with varying fee information, account minimums, or feature descriptions, create ambiguity that AI systems may resolve by reducing recommendation confidence. Consistent, structured entity information across owned and third-party sources is a foundational repair.

Underdeveloped comparison and pricing content. The Pricing and Fees cluster carries $18.1M in modeled commercial opportunity. Brands that lack clear, well-structured, and widely referenced fee and pricing content are underserved in the highest-value prompt cluster in the category.

How CiteWorks Studio Helps

1. Map AI recommendation visibility. Track prompts, platforms, company presence, valid recommendations, top-three and rank-one performance, framing, and citation sources across the buyer journey.
2. Identify the sources shaping AI answers. Find the editorial, review, forum, directory, owned, and search-visible sources that influence brand framing in AI-generated responses.
3. Build the citation architecture plan. Strengthen the public evidence layer so AI systems have more accurate, consistent, and persuasive source material to synthesize into shortlist-quality recommendations.

Commercial Takeaway

AI-led discovery is changing where buyer shortlists are formed in the Roth IRA category. The benchmark shows that Charles Schwab, Fidelity, and Vanguard have built the citation architecture that AI systems reward. They are the primary beneficiaries of shortlist compression, and the gap between these three brands and the rest of the measured universe is widening in recommendation-stage terms, not narrowing.

Brands can lose recommendation-stage visibility even when they are present in AI answers. Robinhood, Betterment, and Wealthfront appear frequently but rarely earn top-three placement. E*TRADE, M1 Finance, and Merrill Edge face an even more severe version of this problem, with presence rates and recommendation rates that are both low and declining in commercial weight relative to the category leaders.

Competitors can intercept demand in high-intent prompt clusters. The Pricing and Fees cluster carries the highest modeled commercial value at $18.1M, and Charles Schwab dominates it with a 51.2% top-three rate. Brands that strengthen their evidence layer in this cluster can improve recommendation-stage visibility at the moment when buyers are making final decisions. Traditional search and source visibility still matter because they contribute to the public evidence layer, but the opportunity is to improve recommendation-stage visibility, not merely chase raw mentions. The brands that win AI recommendations are the brands with the most citable, structured, and trusted evidence at every stage of the buyer journey.

See Where Competitors Are Being Recommended Instead

The Roth IRA category is experiencing shortlist compression, and the gap between visibility and recommendation power is widening. CiteWorks Studio can show where your brand appears in AI-generated responses, where competitors are being recommended in your place, which prompt clusters carry the most commercial risk, which sources are shaping AI answers about your brand, and what needs to change to improve recommendation-stage visibility.

Request an AI Visibility Audit or AI Company Discovery Report to understand your brand's current position in the Roth IRA category and identify the highest-priority gaps in your citation architecture.

Benchmark Source

This analysis is based on the 2026 AI Market Discovery Index for Roth IRAs, published by LLM Authority Index. Read the full benchmark report at the LLM Authority Index website.