Roth IRAs: 2026 AI Market Discovery Index

For the strategic interpretation of this benchmark, read CiteWorks Studio's analysis of how AI search is recommending Roth Iras

Answer Capsule

In the Roth IRA category for June 2026, AI systems are concentrating shortlist recommendations around a small set of established providers. Charles Schwab leads with the highest recommendation coverage and top-three rate across all buyer stages. Fidelity and Vanguard follow as strong challengers. Several brands including E*TRADE, Merrill Edge, and M1 Finance appear in AI responses but rarely earn ranked recommendations, exposing a significant gap between visibility and shortlist eligibility.

Executive Summary

AI platforms are reshaping how investors discover and evaluate Roth IRA providers, and the data shows a clear hierarchy forming. Charles Schwab leads the category with a 51.3% top-three recommendation rate and 55.3% valid recommendation coverage across 1,384 observations. The company captures an estimated $1.86M in monthly AI Authority Value, more than any competitor in the study.

Fidelity and Vanguard occupy the next tier. Fidelity posts a 29.8% top-three rate with an average rank of 1.38, meaning when it is recommended, it tends to appear first. Vanguard achieves a 32.2% top-three rate and the highest raw mention presence at 58.4%, though its average rank of 3.01 suggests it is listed more often than it is placed at the top of AI-generated shortlists.

The most commercially significant finding is the 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 and Wealthfront show similar patterns: high presence, moderate recommendation coverage, and average ranks near 4.0. These brands are recognized by AI systems but are not consistently advanced as top choices.

E*TRADE, Merrill Edge, and M1 Finance face a more severe version of this problem. All three appear in AI responses at meaningful rates but rarely enter the top ten recommendations. Merrill Edge carries a net sentiment score of just 0.44, the lowest in the category, and records negative sentiment in 0.7% of observations.

The AI Discovery Shift in Roth IRAs

AI platforms have become de facto shortlist builders for financial products. When a user asks for the best Roth IRA provider, the AI does not return a comprehensive list of every available option. It selects, ranks, and recommends a small subset based on the evidence it can retrieve, compare, and trust.

This changes the competitive dynamic in a meaningful way. Being a well-known brand with strong search presence is no longer sufficient. The AI must find structured, citable evidence that a provider is suitable for a specific use case. Brands that lack this evidence layer appear in responses as factual references but do not earn recommendation credit.

The difference between being mentioned and being recommended is the central commercial distinction in this market. Charles Schwab, Fidelity, and Vanguard have built the citation architecture that AI systems reward. Other brands carry visibility without recommendation power, which means they are seen in responses but not selected as top choices.

For buyers, AI-generated shortlists are increasingly the first and sometimes only filter applied before deeper research begins. Brands that do not earn recommendation credit in those early moments face a structural disadvantage that is difficult to recover from without addressing the underlying evidence architecture.

Directional Category Leaders

1. Charles Schwab

Charles Schwab leads the Roth IRA category across nearly every measured metric. It appears in 72.6% of all AI responses and earns a valid recommendation in 55.3% of observations. Its top-three rate of 51.3% is the highest in the study, and its average rank of 2.03 means it consistently appears near the top of AI-generated shortlists. Schwab captures an estimated $1.86M in monthly AI Authority Value, representing 5.98% of the total modeled opportunity across all brands.

The public interpretation: Charles Schwab holds the strongest combination of visibility, recommendation coverage, and rank position in the Roth IRA category.

2. Fidelity

Fidelity achieves a 44.7% raw mention presence rate and a 31.1% top-ten recommendation rate. Its average rank of 1.38 is the best in the category, meaning when Fidelity is recommended, it is usually ranked first. The company captures an estimated $1.07M in monthly AI Authority Value and performs particularly well on ChatGPT and Copilot, where it earns top-three rates above 23% and 52% respectively.

The public interpretation: Fidelity is the most trusted first-choice recommendation when AI systems select it, though it appears in fewer responses than Schwab or Vanguard overall.

3. Vanguard

Vanguard appears in 58.4% of all AI responses, the second-highest presence rate in the category. Its top-three rate of 32.2% and top-ten rate of 37.4% place it solidly in the second tier. Vanguard captures an estimated $980K in monthly AI Authority Value. Its average rank of 3.01 suggests it is frequently listed as an option but less often placed at the top of the shortlist.

The public interpretation: Vanguard has strong awareness across AI platforms but is more likely to be included in a list than selected as the primary recommendation.

4. Robinhood

Robinhood appears in 54.1% of AI responses, nearly matching Vanguard in presence. 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 when it does earn a recommendation. Robinhood earns a valid recommendation in 38.6% of observations and captures an estimated $699K in monthly AI Authority Value.

The public interpretation: Robinhood is widely recognized by AI systems but is not consistently recommended as a top-tier Roth IRA provider.

5. Betterment

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 level. Betterment captures an estimated $927K in monthly AI Authority Value, driven partly by strong visibility assist value, and performs best on Gemini and Perplexity.

The public interpretation: Betterment is visible and positively framed across AI platforms but rarely earns top-three placement in Roth IRA shortlists.

6. Wealthfront

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, and its average rank of 4.18 reflects consistent mid-list placement when it does appear. The company captures an estimated $409K in monthly AI Authority Value.

The public interpretation: Wealthfront is a known entity to AI systems but is not positioned as a leading recommendation for Roth IRA selection.

7. SoFi

SoFi appears in 18.8% of AI responses and earns a valid recommendation in 10.2% of observations. Its top-three rate of 3.8% is low, though its average rank of 3.68 is competitive on the occasions when it does appear. SoFi captures an estimated $206K in monthly AI Authority Value.

The public interpretation: SoFi has limited AI recommendation presence in the Roth IRA category and is not yet a consistent shortlist contender across platforms.

8. E*TRADE

E*TRADE 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 it records zero rank-one recommendations on ChatGPT, Gemini, and Google AI Mode. E*TRADE captures an estimated $123K in monthly AI Authority Value.

The public interpretation: E*TRADE is present in AI responses but rarely earns recommendation credit, indicating a gap between brand awareness and shortlist eligibility.

9. M1 Finance

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 across most platforms, though it performs better on Perplexity, where it achieves a 9.4% rank-one rate. M1 Finance captures an estimated $130K in monthly AI Authority Value.

The public interpretation: M1 Finance has niche recommendation strength on specific platforms but lacks the broad shortlist coverage needed to compete in the main category tier.

10. Merrill Edge

Merrill Edge appears in 9.4% of AI responses, the lowest presence among all measured brands. It earns a valid recommendation in only 3.5% of observations. Its net sentiment score of 0.44 is the lowest in the study, and it is the only brand to record negative sentiment, appearing in 0.7% of observations with an unfavorable signal. Merrill Edge captures an estimated $66K in monthly AI Authority Value.

The public interpretation: Merrill Edge has the weakest AI recommendation position in the Roth IRA category, with low presence, low recommendation coverage, and the lowest sentiment score in the study.

The Buying Moments That Now Decide the Category

Best Brokerage and Investment Platform Discovery

This awareness-stage cluster generated 476 observations and represents a modeled opportunity of $6.4M. Charles Schwab leads with a 46.6% top-three rate and 48.3% top-ten rate. Betterment leads in captured value at $553K, driven by strong visibility assist. Fidelity and Vanguard also perform well at this stage. This cluster captures buyers at the earliest point of provider research, where first impressions in AI-generated responses carry outsized influence on which brands enter the consideration set.

Brokerage and Investment Platform Comparisons

This consideration-stage cluster generated 437 observations with a modeled opportunity of $6.6M. Charles Schwab dominates with a 56.5% top-three rate and 59.7% top-ten rate. Fidelity follows at 30.4% top-three. This cluster represents buyers actively comparing providers side by side, making recommendation rank especially important. Brands that appear here but rank poorly are effectively advertising for the brands ranked above them.

Brokerage and Investment Platform Pricing and Fees

This decision-stage cluster generated 471 observations and carries the largest modeled opportunity at $18.1M. Charles Schwab leads with a 51.2% top-three rate. Fidelity and Vanguard follow at 32.7% and 34.8% respectively. Buyers in this cluster are evaluating final decisions based on cost and fee structures, where top recommendation placement carries the highest immediate commercial value of any cluster in the study.

Why Recommendation Power Is Concentrating

AI systems do not recommend brands randomly. They retrieve evidence from structured sources, compare options, and rank based on the strength and consistency of available information. In the Roth IRA category, the evidence layer that drives recommendations includes official brand content, comparison articles, review publications, community discussions, and regulatory or consumer trust signals.

Charles Schwab, Fidelity, and Vanguard benefit from deep, consistent citation coverage across these source types. Their brand content is widely indexed, their fee structures are clearly documented, and they appear in comparison and review content that AI systems treat as sufficiently authoritative to support a recommendation.

Brands with weaker recommendation coverage tend to lack one or more of these evidence layers. They may appear in factual references but not in the comparison content that earns recommendation credit. Strong brand awareness does not substitute for structured data that AI systems can retrieve and evaluate with confidence.

The result is a concentration of recommendation power around a small group of providers that have built the evidence architecture AI systems rely on, and a growing gap for brands that have not.

The Category's Most Visible Warning Sign

Merrill Edge presents the clearest warning sign in the category. It appears in only 9.4% of AI responses, the lowest presence among all measured brands, and earns a valid recommendation in just 3.5% of observations. Its net sentiment score of 0.44 is the lowest in the study, and it is the only brand to record negative sentiment signals.

On Google AI Mode, Merrill Edge appears in 15.8% of responses but earns a valid recommendation in only 2.5% of those cases. Its average rank on that platform is 5.5, meaning even when it does appear in a shortlist, it sits near the bottom.

This is not a brand awareness problem. Merrill Edge is a recognized institution in the brokerage category. The issue is recommendation architecture. The evidence that AI systems need to rank Merrill Edge as a suitable Roth IRA choice is not sufficiently present, structured, or trusted across the source layers that drive shortlist placement. Presence and reputation, without the right evidence layer, do not translate into recommendation credit.

What This Means for the Category

The Roth IRA category is experiencing shortlist compression. AI platforms are concentrating recommendations around a small set of providers with the strongest evidence layers. Charles Schwab, Fidelity, and Vanguard are the primary beneficiaries. Every other brand in the study faces some degree of recommendation gap, ranging from moderate underperformance to near-complete shortlist exclusion.

Competitor displacement is accelerating as a result. Brands that appear in AI responses but do not earn recommendation credit are losing consideration opportunities to the brands ranked above them. E*TRADE, Merrill Edge, and M1 Finance are the most exposed, but Robinhood, Betterment, and Wealthfront also show signs of presence without proportionate recommendation return.

Trust-source dependency is becoming a structural advantage for category leaders. The brands that win AI recommendations are not necessarily the lowest-cost providers or the most innovative platforms. They are the brands with the most citable, structured, and trusted evidence across the source types that AI systems use to build shortlists.

For brands that underperform in AI recommendations, recovery requires stronger entity architecture, better structured content, deeper citation coverage across comparison and review sources, and more clearly organized evidence around buyer-stage queries. Visibility alone is not enough to change the recommendation outcome.

What This Public Benchmark Does Not Include

- Full cluster dataset covering all 10 buyer-stage clusters

- Prompt-level response tables showing exactly which queries each brand wins or loses

- Citation-source failure maps identifying which evidence layers are missing by platform

- Platform-by-platform recovery priorities for each brand

- Entity and schema diagnostics for AI discoverability

- Source-layer gap analysis covering comparison, review, and trust content

- Company-specific content recommendations

- Exact competitor threat profiles for each brand

- Full paid opportunity model with platform-level valuation

This page shows the market shape. The paid report shows the repair map.

Methodology and Disclaimers

1. Market studied: Roth IRA providers and brokerage platforms offering Roth IRA accounts.

2. Brands and 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 and window: June 2026, snapshot-based.

4. AI platforms tested: ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, and Perplexity.

5. Observations analyzed: A total of 1,384 observations were analyzed across three public high-intent clusters. Prompt count was not disclosed in the public dataset.

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 position.

8. Definition of a valid recommendation: A valid recommendation is a positive, shortlist-quality recommendation that earns recommendation credit in an AI-generated response. Visibility is not the same as recommendation credit.

9. Ranking and 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, comprising AI Recommendation Value and AI Visibility Assist Value.

10. Limitations: This is a point-in-time benchmark. AI outputs can change as models are updated or retrained. Modeled opportunity values are estimates based on commercial intent proxies and are not revenue figures. This report is not a full audit and does not represent a complete market census.

For a Company-Specific Authority Index Report

For a company-specific Authority Index report, the deeper analysis would show which prompts each company wins or loses, which AI platforms are under-recognizing the brand, which source layers are shaping recommendations, and what changes may improve AI shortlist eligibility.