Google AI vs ChatGPT: 62% split on brand picks and the surprising triggers

AI Search Brand Mentions: 62% Disagreement Between Google AI and ChatGPT

Optimize for Google AIO and ChatGPT: Executive Snapshot

Across tens of thousands of identical queries run on ChatGPT, Google AI Overviews (AIO), and Google AI Mode, BrightEdge finds a 61.9% disagreement in brand recommendations. Only 33.5% of queries returned the exact same brands across all three platforms [S1].

• Disagreement rate: 61.9% across platforms; agreement on exact brands in 33.5% of queries [S1].
• Mentions per query: Google AIO averaged 6.02 brand mentions vs. ChatGPT at 2.37 - about 2.5x more [S1].
• High-intent triggers: Queries with "buy," "where," or "deals" yielded brand mentions 65% of the time across platforms [S1].
• Vertical skew: Online retail and finance reached at least 40% brand-mention coverage across all three platforms [S1].
• "Best" queries: Comparison requests for "best" products produced brand citations in 43% of cases across platforms [S1].
• Implication for marketers: Treat each AI surface as a distinct recommendation engine and build for multi-surface citation, not just classic rankings [S1][S2].

Method & Source Notes

BrightEdge measured brand citations using its AI Catalyst tool by issuing tens of thousands of the same queries to ChatGPT, Google AI Overviews, and Google AI Mode, then comparing which brands were suggested and how often [S1]. The analysis covered multiple verticals and intents (e.g., "buy," "where," "deals," and "best") [S1].

Google notes that AI Overviews are generated by large language models and include links to underlying sources from the open web, which helps explain higher link density in AIO outputs. See About AI Overviews in Search [S2].

OpenAI states that ChatGPT is trained on a mixture of licensed, publicly available, and human-created data and can browse the web in certain modes, so brand mentions may come from training data and live retrieval depending on configuration. See OpenAI policies and the OpenAI Help Center [S3].

Key limitations:

• BrightEdge did not publish the exact query list or breakdown by geography or device; results may differ by market or device [S1].
• AI outputs are dynamic and can vary by session and over time; aggregate rates do not guarantee reproducibility for specific queries [S1].
• "Google AI Mode" is a BrightEdge-defined surface with limited public documentation [S1].
• Differences in browsing or grounding settings for ChatGPT can materially change outputs [S1][S3].

AI search brand mentions: what changes across platforms

BrightEdge finds strong divergence in which brands are cited across the three AI platforms [S1]. AIO shows significantly more brand links per response (6.02 per query) than ChatGPT (2.37), while Google AI Mode tends to be more selective than both [S1]. This aligns with Google's product design for AI Overviews, which places multiple links in responses to let users explore corroborating sources. See About AI Overviews in Search [S2].

BrightEdge's pattern analysis indicates:

• ChatGPT often references well-known brands even without live grounding, implying reliance on model training data associations [S1][S3].
• Google AIO emphasizes breadth, citing about 2.5x more brands than ChatGPT for the same queries [S1].
• Google AI Mode surfaces fewer brands but with heavier citation backing, suggesting stricter selection [S1].

High-intent queries - including "buy," "where," and "deals" - trigger brand mentions about 65% of the time across platforms, signaling that commercial intent remains a strong driver of brand visibility in AI responses [S1]. Vertical effects are notable: online retail and finance achieve at least 40% brand-mention coverage across all three platforms, consistent with clearer commercial intent and richer third-party corroboration in those categories [S1]. "Best" product comparisons yield brand citations 43% of the time, indicating that mid-funnel discovery queries also produce brand exposure in AI answers [S1].

Interpretation & Implications

Likely: Classic SEO that earns inclusion and citations from high-quality pages supports visibility in Google AI Overviews because AIO links to web sources surfaced from search and displays multiple citations per answer [S2]. Consolidating topical authority with precise, corroborated content can increase chances of earning one of AIO's limited link slots [S2].

Likely: High-intent query coverage still matters. Content that directly addresses transactional language ("buy," "where," "deals"), with clear product or offer details and structured data where appropriate, aligns with the 65% brand-mention trigger rate [S1].

Tentative: ChatGPT's brand mentions often reflect training data salience. To appear more frequently, brands likely need durable, well-distributed mentions across reputable sources - press, reviews, and third-party references that persist long enough to be captured in training cycles [S1][S3].

Tentative: Treat Google AI Mode as a stricter recommender. Earning presence may require dense third-party validation and cross-site corroboration (for example, expert reviews, standards bodies, authoritative directories), given BrightEdge's observation of fewer but more heavily supported brand picks [S1].

Speculative (based on BrightEdge's framing): A potential "citation network effect" - visibility gained on one surface can reinforce presence on others over time, especially where outputs rely on overlapping web evidence and recognizable entities [S1]. This would favor consistent, cross-site coverage over isolated content pushes.

Channel mix: where to allocate effort

• Pages engineered for corroboration (clear claims, sourcing, and comparisons) to improve AIO link eligibility [S2].
• Durable third-party references (reviews, industry lists, product specs on distributors) to support both ChatGPT salience and Google surfaces [S1][S3].
• Intent-aligned content for commercial queries that the study shows frequently trigger brand mentions [S1].

Contradictions & Gaps

• Sampling opacity: The exact query corpus, category weighting, and geography are not disclosed; results may vary by market or device [S1].
• Output variability: AI answers can vary run to run; the study's aggregate rates do not guarantee reproducibility for specific queries [S1].
• Definition of "Google AI Mode": Public technical detail is limited, so inferences about its selectivity beyond BrightEdge's observations should be considered tentative [S1].
• Training vs. retrieval in ChatGPT: The degree to which any single response is training-driven vs. browse-driven depends on mode and session settings; evidence of training-bias effects warrants cautious generalization [S1][S3].

Data Appendix: raw figures and thresholds

• Overall disagreement across three platforms: 61.9% [S1].
• Exact same brands across all three: 33.5% of queries [S1].
• Average brand mentions per query: Google AIO 6.02; ChatGPT 2.37; Google AI Mode fewer than both (exact mean not provided) [S1].
• High-intent query trigger rate ("buy," "where," "deals"): 65% produce brand mentions across platforms [S1].
• "Best" queries (comparisons): 43% produce brand citations across platforms [S1].
• Verticals with at least 40% brand-mention coverage across all three: online retail and finance [S1].

Sources

• [S1] BrightEdge. ChatGPT vs Google AI: 62% Brand Recommendation Disagreement. Method: tens of thousands of identical queries across ChatGPT, Google AI Overviews, and Google AI Mode; analysis by query intent and vertical. August 2025.
• [S2] Google Search Help. About AI Overviews in Search. Product overview stating that AI Overviews are generated by LLMs and include links to supporting sources.
• [S3] OpenAI. How we train and use data and related model documentation; browsing capability in certain modes noted in the OpenAI Help Center.