Welcome to episode 23 of The GEO Show, the briefing on generative engine optimization, LLM citations, and AI search visibility. Brought to you by GEOforge.
In this episode, Paris Childress, founder of Hop AI and co-founder of GEOforge, records a September 8, 2026 solo news roundup. The lead story is Semrush and Exploding Topics finding that 57.5% of AI users said chatbot information caused them not to buy, then nine more beats on mentioned versus chosen, single-check misclassification, Reddit citation collapse, five-engine source overlap, Google AI Mode citation architecture, production UI collection APIs, Claude retrieval versus citation, historical AI data moats, and Mastercard's agent shopping forecast.
In this episode:
π Semrush/Exploding Topics: AI talks buyers out of purchases
A vendor-sponsored Semrush and Exploding Topics survey of 2,338 US adults (Sept 7) found 57.5% of AI users said chatbot information caused them not to buy. 65% said AI had at least partially replaced Google for product research. Among weekly AI users, 73.6% bought from an organic AI recommendation. Paris's GEO take: purchase exclusion, reputation, and buyer risk now sit beside discovery.
π Latent Space: mentioned is not chosen
Latent Space's Frontier AI visibility tracker (6,762 answers, 161 categories) shows Kysely in 42/42 answers but first choice only once. GPT-5.6 Sol and GPT-6 Astra category leaders differed in 33/121 comparable categories. Mention share of voice can overstate commercial visibility; recommendation role and sentiment matter.
π² Surfaced by: one AI check can misclassify visibility
Across 536 combinations with at least five checks, 80% of appearance cases were inconsistent; a single check matched the majority only 72.2% of the time on inconsistent sets. Paris: target roughly 30 to 40 runs per prompt before volatility flattens.
π Surfaced by: ChatGPT Reddit citations 37.9% to 3.4%
In fixed cohorts across two 14-day windows, ChatGPT Reddit citations collapsed while Perplexity (50% to 44.7%) and Gemini (30.8% to 33.7%) stayed comparatively stable. Off-site GEO must be engine-specific.
π Intender: only 8% source overlap across five engines
27,924 citations from 3,600 searches: 8% overlap across all five platforms; 71% of 4,625 domains on one engine only; 65 domains in all five. No single AI citation ecosystem.
πΊοΈ Nicholas Sitter: Google AI Mode citations flip by question type
Entity-wrapped citations: 87.6% for recommendation-style prompts, 19.8% for explanatory prompts, 0% for generic informational queries. Tag and report prompts by buyer stage and intent.
π querying.ai: production UI collection as a GEO API
Real customer-facing responses across seven surfaces from about $20/month, not model API output. Paris: UI and API results diverge in testing; API-only trackers can mislead.
π§ͺ Is My Brand in AI: Claude retrieval vs citation
Named-brand sites retrieved in only 33% of run-brand pairs via Claude API web search; 62.7% of retrieved domains cited. Shaky design (API + three runs), but retrieval and citation still need separate metrics.
ποΈ Similarweb: historical AI visibility as a data moat
If nobody sampled an answer when it happened, replaying the prompt today does not reconstruct it. Historical depth separates data companies from prompt trackers.
π€ Mastercard: AI agents shopping and paying by 2030
Forecast: 300M-plus shoppers may routinely use AI agents to shop and pay by 2030. Teen trust signals already lean agentic. Paris: build agent-ready sites across discovery, recommendation, selection, and transaction.
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π¬ Question: If AI is already talking buyers out of purchases, does your GEO reporting still stop at whether you were mentioned?