Every LLM Is Its Own Media Channel

Summary

Why Marketers Must Treat ChatGPT, Gemini, and Claude as Distinct Discovery EcosystemsAIVO Journal • October 7, 2025Executive SummaryMost marketers still optimise for “AI visibility” as if large language models (LLMs) share a single index.They do not.Each assistant has its own ingestion pipeline, retrieval weighting, and update cadence. Treating them as one channel is equivalent to running the same media plan across Google, Meta, and TikTok without accounting for format or audience.The result: wasted spend, inconsistent presence, and unmeasurable leakage.1 — Three Engines, Three EconomiesChatGPT-4o / o1 now weights recency and verified provenance more than raw citation count. Its retrieval layer privileges timestamped, human-moderated, and licensed sources (especially those in the “trusted publisher” feed). Temporal freshness remains a key visibility factor.Gemini 1.5 Pro continues to anchor on entity-linked data but now integrates cross-modal context from Google’s Vertex + Knowledge Graph stack. Visibility therefore depends on structured entity mapping and schema-compliant attribution, not keyword proximity.Claude 3.5 Sonnet / Opus applies relevance scoring tuned for semantic reliability and value alignment, penalizing over-optimized or speculative content. Its heuristic filter favours expert-curated, neutrally phrased, and safety-audited material over high-traffic sources.Each assistant therefore defines “credible visibility” differently. A brand ranking high in Gemini’s ecosystem may be invisible in Claude’s due to conservative guardrails, or replaced in ChatGPT answers because of conversational bias.2 — Why Convergence Won’t HappenDespite similar foundation architectures, convergence is unlikely.Two constraints prevent it:Data sovereignty – model owners cannot share ingestion without breaching licensing or trust frameworks.Governance asymmetry – each model operates under different compliance and regional mandates (US AI Act vs EU AI Act alignment).LLMs are diverg...