Why is podcast audio invisible to AI engines?
Because the retrieval corpus is text. ChatGPT's crawlers, Google's index and Perplexity's real-time fetchers all consume written words; none of them listen to your episodes when assembling an answer. Your best interview — the one where your guest explains the concept better than anyone ever has — contributes zero to AI visibility unless those words exist as crawlable text. This is the audio case of the principle in video and podcast GEO: the text layer is the asset.
What does a citable transcript look like?
- Cleaned, not raw. Auto-transcription garbles names, numbers and terminology — the exact tokens that matter for retrieval and grounding. Correct them.
- Speaker-labelled. Engines attribute claims to people; “Dr. A:” before each turn makes the named-claim structure machine-readable, reinforcing author entities.
- Sectioned with question-shaped headings. Break the transcript at topic shifts and give each section an H3 phrased as the question being discussed. Each section becomes a standalone answer block per answer block writing.
- Fact-checked against your other content. If a guest cites a statistic that contradicts your site, add an editor's note — grounding will find the conflict either way, per grounding fact consistency.
How should show notes be structured?
Show notes are the episode's retrieval summary, and most shows waste them on a two-line teaser. Write them as a document that stands alone: a 40-80 word summary with the episode's core claims; a guest bio with the guest's canonical title (consistent with their own sites); a key-topics list with timestamps mapped to the transcript's sections; every statistic or claim mentioned, restated in text with its source; and links to resources discussed. The show-notes page is frequently the passage engines extract even when a full transcript exists, because it is the most answer-dense text on the page.
Where should transcripts live?
On your own domain, one permanent URL per episode, linked from an indexable show archive — not only inside podcast platforms, whose pages are thin and whose structured data you do not control. Platform pages matter for distribution, but your episode page is the citation target you can shape: add Article or PodcastEpisode structured data, internal links to related articles, and a visible publication date per structured data for AI and content freshness. If you repurpose episodes into articles — covered in webinar and event repurposing — link the siblings together.
What is the scale of the opportunity?
Explainer and expert-opinion questions are core AI answer territory: ChatGPT serves roughly 900 million weekly users (OpenAI, 2026) and Perplexity passes 1 billion monthly queries with sentence-level citations. When someone asks an engine for the expert view your guest gave you, the engine cites whoever published that view as text. If the only text is a rival's summary of your episode, they get the citation for your content. With roughly 68% of search sessions ending zero-click (Ahrefs, February 2026), the quoted passage is often the entire audience interaction.
What pitfalls keep podcasts invisible?
First, publishing audio only — still the norm, and still a total retrieval loss. Second, dumping raw transcripts without cleanup: messy text misquotes guests and fails grounding. Third, transcripts hidden behind accordions, tabs or PDFs that crawlers handle inconsistently. Fourth, episode pages on platforms that block or throttle certain crawlers. Fifth, no named-claim hygiene: an episode where the guest's title differs from everywhere else on the web weakens attribution. Finally, abandoning the archive: older episodes answering evergreen questions are durable citation assets, so keep their pages live and occasionally refreshed. Track outcomes by adding the questions your best episodes answer to the fixed 50-100 question measurement set, run monthly with screenshots, and watch whether citations point at your episode pages or at someone else's summary.