Public AI Market Strategy Report - Online Stock Brokers

Answer Capsule

Public appears in 14.2% of AI responses across the Online Stock Brokers category but earns valid recommendations in only 4.8% of them, revealing a significant gap between visibility and shortlist eligibility. The benchmark shows Public is present in AI-generated answers but rarely positioned as a top recommendation, with a 1.49% top-three rate and an average recommended rank of 4.57. Public's clearest win is a strong positive framing signal on Google AI Overviews, where sentiment and recommendation coverage are both meaningfully higher than any other platform. The clearest weakness is the near-complete absence of recommendation-stage visibility on Gemini, where a 12.35% raw mention presence rate produces only 0.82% valid recommendation coverage. The clearest opportunity is building the citation and source-layer evidence needed to convert neutral references into positive shortlist positions, particularly in the Discovery cluster.

Who This Report Is For

This report is for Public's marketing, growth, and product leadership teams evaluating how AI-led discovery is shaping investor consideration and where the brand stands relative to competitors in AI-generated broker recommendations.

Report Card

• Report type: AI Company Market Strategy Report
• Target company: Public
• Category / market studied: Online Stock Brokers
• Reporting month: June 2026
• AI platforms tracked: ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, Perplexity
• Public high-intent clusters: 3 (Discovery, Comparison, Pricing Evaluation)
• AI observations analyzed: 1,479
• Competitors tracked: Charles Schwab, Fidelity, Robinhood, Interactive Brokers, Vanguard, Webull, E*TRADE, Tastytrade, Merrill Edge

Executive Summary

Public has a measurable presence in AI-generated broker responses but is not being recommended at a rate that reflects that presence. The June 2026 LLM Authority Index benchmark shows Public appears in 14.2% of all AI responses across six platforms, yet earns valid recommendations in only 4.8% of them. Public is mentioned in AI answers roughly one in every seven times a broker is discussed, but it is shortlisted as a positive option less than one in twenty times.

The gap between presence and recommendation power is the defining finding of this report. Public receives 88 positive mentions, 120 neutral mentions, and 2 negative mentions across 1,479 observations. The net sentiment score of 0.4095 indicates that when Public is mentioned, it is more often neutral than positive. The average recommended rank of 4.57 means that even when Public earns recommendation credit, it appears well below the leading contenders in AI-generated shortlists.

Public's strongest cluster is Discovery, where it captures the largest share of its modeled monthly AI Authority Value. The weakest cluster is Pricing Evaluation, where Public appears in only 10.36% of responses and earns a 3.81% valid recommendation coverage rate. Pricing Evaluation carries the highest buyer-stage multiplier across the three clusters, making this the most commercially costly gap.

Across all platforms, Public's strongest signal comes from Google AI Overviews, where it achieves a 9.88% valid recommendation coverage rate and a 0.7714 net sentiment score, the highest sentiment score of any platform in this dataset. The clearest platform gap is Gemini, where a 12.35% raw mention presence rate collapses to 0.82% valid recommendation coverage and a net sentiment score of 0.10. Public is being retrieved on Gemini but is almost never recommended.

Competitor displacement is severe. Charles Schwab captures 43.2 times more AI Authority Value than Public. Robinhood captures 17.5 times more. Even Webull, a direct competitor in the commission-free space, captures 8.5 times more. Public's captured share of the category's modeled AI opportunity is 0.32%, meaning 99.68% of category value flows to competitors.

What Public Is Winning

Public shows a narrow but meaningful recommendation pocket on Google AI Overviews. On this platform, Public achieves a 9.88% valid recommendation coverage rate and a 0.7714 net sentiment score. When Google AI Overviews retrieves Public, the framing is predominantly positive. The 24 valid recommendations on Google AI Overviews represent approximately 33.8% of Public's total valid recommendation count across all platforms, making it the single most productive platform for recommendation conversion.

Public also shows a relatively strong positive-to-neutral ratio on Copilot, where 34 of 42 mentions are positive, yielding a 0.8095 net sentiment score. The evidence suggests that when Copilot surfaces Public, it tends to frame the brand favorably. Recommendation coverage remains low at 11.34%, but the positive framing ratio is the highest of any platform in absolute terms, indicating that the underlying source material Copilot retrieves about Public is not hostile to the brand.

These two platform signals together indicate that positive framing is achievable for Public when the right source material is available. The challenge is that neither platform is converting that positive framing into consistent top-three or rank-one recommendation positions.

Where Public Has the Clearest AI Visibility Gaps

Public's most significant gap is the conversion of presence into recommendation credit. A raw mention presence rate of 14.2% drops to a valid recommendation coverage rate of 4.8%. Public is being retrieved by AI systems as a factual reference or neutral listing, but is not being positioned as a recommended option for investors. The gap is most severe on Gemini, where Public appears in 12.35% of responses but earns valid recommendations in only 0.82% of them, a conversion rate of roughly 1 in 15.

The top-three rate of 1.49% and rank-one rate of 0.54% confirm that Public is almost never positioned at the top of AI-generated shortlists. When Public does earn recommendation credit, it appears at an average rank of 4.57, placing it behind the leading competitors in the majority of AI responses. For buyers who rely on AI-generated shortlists, this positioning means Public is functionally absent from the decision moment.

The Pricing Evaluation cluster is Public's weakest buyer stage. A 10.36% presence rate and 3.81% valid recommendation coverage rate in this cluster means Public is missing the prompts where purchase intent is highest. Buyers asking AI platforms about fees, pricing structures, and cost comparisons are the most commercially ready segment, and Public is being displaced almost entirely in that context.

Competitor displacement by category leaders compounds the problem. Charles Schwab, Fidelity, and Robinhood collectively occupy the top recommendation positions across most clusters and platforms. For buyers moving from initial discovery to shortlist comparison, Public rarely appears as a named option alongside these competitors. The 0.32% captured share of AI opportunity makes clear that this is not a marginal gap.

Biggest Opportunity

Public's single biggest opportunity is converting neutral references into positive recommendations in the Discovery cluster. Discovery represents the largest modeled opportunity in this category, and Public already appears in 14.8% of Discovery prompts. However, only 4.0% of those appearances result in valid recommendations. This means Public has achieved retrieval without achieving recommendation. The source-layer evidence that AI systems use to evaluate whether Public is a positive option is not yet strong enough to support shortlist placement.

The path from reference to recommendation requires a stronger citation architecture built around the features that differentiate Public: social investing, bond access for retail investors, and transparency around payment order flow. Google AI Overviews demonstrates that when AI systems have access to the right source material, Public can earn positive recommendations. The opportunity is to replicate those source conditions across more platforms and more prompt types within the Discovery cluster, before addressing the harder conversion gap in Pricing Evaluation.

Prompt Evidence

Google AI Overviews / Discovery Prompt: "What is the best brokerage for beginners?" Result: Public was mentioned positively but did not achieve a top-three ranked position in the AI-generated response.

Copilot / Discovery Prompt: "Which online broker is best for social investing?" Result: Public received a positive mention with recommendation credit, appearing at rank 5 in the response.

Gemini / Comparison Prompt: "Compare Public vs Robinhood for commission-free trading" Result: Public was listed as a neutral factual reference without receiving valid recommendation credit.

Perplexity / Pricing Evaluation Prompt: "Which broker has the lowest fees for stock trading?" Result: Public did not appear in the AI-generated response.

What CiteWorks Studio Would Do Next

Phase 1: AI Market Discovery Audit Map Public's full prompt-level presence across all six platforms and identify the specific prompts where neutral references are closest to converting into valid recommendations.

Phase 2: Recommendation Readiness Plan Identify the citation sources, comparison articles, and editorial content AI systems are currently retrieving for Public and determine which source-layer gaps are suppressing recommendation conversion on Gemini, ChatGPT, and Perplexity.

Phase 3: Owned Answer Layer Buildout Strengthen Public's owned content around social investing, bond access, and commission-free trading to give AI systems more retrievable material for positive framing in Discovery and Comparison prompts.

Phase 4: Citation / Authority Layer Development Build third-party comparison coverage, editorial placements, and review signals that position Public as a recommended option for specific buyer profiles rather than a neutral factual listing.

Phase 5: Monthly AI Visibility and Recommendation Tracking Monitor Public's valid recommendation coverage rate, top-three rate, and net sentiment score across platforms monthly to measure progress and identify new displacement risks as the competitive landscape shifts.

Why This Matters

Public is present in AI responses but is not winning the buyer shortlist. When an investor asks an AI platform for the best brokerage for social investing, the best commission-free broker, or the best platform for beginner investors, Public is often retrieved as a factual reference but rarely positioned as a top recommendation. Presence without recommendation credit means the brand absorbs no meaningful share of the commercial intent that drives those queries.

The gap between presence and recommendation power is measurable and addressable. Public does not need to appear in more AI responses. It needs to convert the appearances it already has into positive, ranked recommendations. The evidence from Google AI Overviews demonstrates that positive framing is achievable when the right source material supports it. The next move is building the citation and content architecture that turns neutral references into shortlist positions, before competitors consolidate the Discovery and Comparison clusters further.

Core Metrics

• Mentions: 210
• Valid recommendations: 71
• Top 3 recommendation count: 22
• Rank 1 recommendation count: 8
• Average recommended rank: 4.57
• Positive mentions: 88
• Neutral mentions: 120
• Negative mentions: 2
• Raw mention presence rate: 14.2%
• Valid recommendation coverage: 4.8%
• Top 3 recommendation rate: 1.49%
• Rank 1 recommendation rate: 0.54%
• Strongest cluster by recommendation behavior: Discovery (C01)
• Strongest platform by recommendation behavior: Google AI Overviews

Sentiment Score

Sentiment Score = (positive mentions x 1 + neutral mentions x 0 + negative mentions x -1) / total mentions

Public's sentiment score is (88 x 1 + 120 x 0 + 2 x -1) / 210 = 86 / 210 = 0.4095.

This score matters because unclassified mention counts are misleading. Public has 210 total mentions, but only 88 of them are positive. The remaining 120 neutral mentions and 2 negative mentions carry no recommendation credit and do not contribute to shortlist placement. Share of voice is a diagnostic metric, not a business KPI. A positive recommendation, a neutral reference, a cautionary mention, and a competitor-displaced mention are not equivalent outcomes. Counting all mentions as wins produces a false read on AI visibility. Classified sentiment is required before drawing any meaningful conclusion about how AI platforms are treating the brand.

Sentiment by Platform

Methodology

1. This is an AI Company Market Strategy Report based on June 2026 LLM Authority Index benchmark data for the Online Stock Brokers category. It is benchmark-based analysis, not a client implementation result.
2. The reporting window is June 2026, based on a point-in-time snapshot of AI platform outputs. AI outputs can change with model updates, data source changes, and content shifts.
3. Platforms tracked: ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, and Perplexity.
4. Total observations analyzed: 1,479 across three public high-intent clusters. Unique prompt count was not available in the public version of this dataset.
5. Competitor universe: Charles Schwab, Fidelity, Robinhood, Interactive Brokers, Vanguard, Webull, E*TRADE, Public, Tastytrade, and Merrill Edge. This universe covers major U.S. brokers but is not a full market census.
6. Prompt clusters used: Discovery (awareness-stage), Comparison (consideration-stage), and Pricing Evaluation (decision-stage). These three clusters represent a subset of the full benchmark. The public version covers 3 of 10 total clusters.
7. The Stage 0 extraction layer identified raw AI outputs, which were then classified by mention type, sentiment, and recommendation status before metrics aggregation.
8. A mention is defined as any appearance of a company name in an AI-generated response, regardless of sentiment, rank, or recommendation status.
9. A valid recommendation is defined as a positive, shortlist-quality recommendation that earns recommendation credit. Neutral references, cautionary mentions, and factual listings are not counted as valid recommendations. This distinction is the basis of the presence-versus-recommendation-power analysis.
10. Modeled values, including monthly AI Authority Value, AI Recommendation Value, and AI Visibility Assist Value, are estimates based on commercial intent proxies applied to recommendation position and cluster weighting. These are not revenue figures, pipeline values, or realized business outcomes.
11. Ahrefs data, where referenced, is used as supporting evidence for organic search visibility, backlink strength, and source-layer retrievability. It does not override LLM Authority Index AI recommendation metrics and does not independently prove AI recommendation influence.
12. Company names are normalized throughout. Any taxonomy conflicts between dataset labels and published company names have been resolved using the safest available interpretation.

See How AI Is Recommending Your Brand

The benchmark identifies which brokers are winning AI shortlists and which are being retrieved but not recommended. For brands like Public, where presence is measurable but recommendation conversion is low, the gap is specific and addressable at the prompt, page, and citation layers. CiteWorks Studio can map where your brand appears, where competitors are recommended instead, which prompts carry the most commercial risk, and what source-layer changes are needed to improve recommendation-stage visibility.