These notes are based on the YouTube video by Content and Conversation

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Key Takeaways

• AI‑native search (AEO) is moving from “watch‑and‑wait” to “take‑action.” Marketers now need concrete tactics to win visibility on ChatGPT, Perplexity, Google AI Overviews, etc.
• Profound’s Conversation Explorer provides one of the largest query‑volume datasets for answer‑engine searches. It aggregates tens of millions of queries per month from ChatGPT, Perplexity, Bing, and soon Google AI.
• Generative queries dominate AI search. Roughly 50 % of all answer‑engine queries are purely generative (no clear commercial intent), while traditional informational, navigational, and transactional intents shrink to a small slice.
• Click‑through rates and conversion quality differ wildly by platform. Perplexity yields 6‑10× higher CTR than ChatGPT, and conversion rates of 20‑30 % have been anecdotally reported for high‑intent traffic.
• Content format matters more than ever. Listicles and comparative tables account for ~33 % of citations, while standard blogs are under 10 %. Structured, data‑rich content is favoured by LLMs.
• Technical SEO signals still matter, but they need AI‑specific twists:
  • Year‑stamp URLs (e.g., …/best‑laptops‑2025) can boost citations by 20 %+ in ChatGPT.
  • Author schema has shown the strongest uplift among schema types.
  • lms.txt / lmsfull.txt files dramatically improve answer‑engine pickup (5‑10× on some platforms).
• Freshness decays fast: most AI‑search citations drop to baseline after 1‑2 months; evergreen, problem‑oriented landing pages stay relevant longer.
• Profound’s roadmap: content‑brief generation and in‑platform content creation to turn massive query data into actionable, optimized copy.
• Pricing: a self‑serve tier is now available at $500 / month, making the platform more accessible to mid‑market brands.

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Core Concepts

AEO vs. SEO

Aspect	SEO	AEO (AI‑native Search)
Primary engine	Google/Bing classic SERP	LLM‑driven answer engines (ChatGPT, Perplexity, Google AI)
Intent distribution	Dominated by informational / transactional	~50 % generative, “no‑intent” queries
Optimization muscles	Meta tags, backlinks, site speed	Same muscles (titles, schema, URLs) plus AI‑specific tricks (year‑stamp, semantic chunking)
Adoption curve	Near‑saturation	Rapidly rising, still low‑to‑mid adoption

Takeaway: AEO is not a brand‑new discipline; it’s SEO repurposed for a different query distribution and retrieval model.

🔗 See Also: What Is AEO? How to Get Your Brand Found in AI Search

Generative vs. Traditional Intent

• Generative queries – users ask the model to create something (e.g., “create a spreadsheet of employee hours”).
• Traditional intent – classic informational, navigational, or transactional queries.
• In AI search, generative queries outnumber traditional intents, forcing brands to think about “who will the model cite?” rather than just “who will click the link?”.

Retrieval‑Augmented Generation (RAG)

• Current LLMs (e.g., ChatGPT) pull documents from an external index (Bing for OpenAI, Google’s index for Gemini).
• If your page isn’t in Bing, it won’t surface in ChatGPT.
• The RAG pipeline is built by academia; it’s not SEO‑aware, so brands must shape their content to be RAG‑friendly.

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Tactical Recommendations

1. URL & Title Optimization

• Add a future year token (2025) to URLs, title tags, and meta descriptions for a ~20 % citation lift in ChatGPT.

  /best-corporate-credit-cards-2025

• Prefer URL placement over title‑tag placement for the biggest impact (blue‑ocean opportunity).

2. Cross‑Platform Publishing

• Republish key posts on LinkedIn Pulse (or other authoritative platforms) to create multiple citations for the same content, increasing LLM confidence.

3. Semantic Chunking & Structured Answers

• Write self‑contained, data‑driven paragraphs that answer a single long‑tail query.
• Use HTML tables for comparative data (e.g., “Best laptops – 7 dimensions”).
• Goal: give the answer engine a “ready‑made snippet” to pull.

4. Schema.org Enhancements

• Author schema shows the strongest uplift for answer‑engine pickup (observed ~2‑3 % increase).
• Standard schema (FAQ, Breadcrumb, Product) still valuable but less impactful than author markup in early tests.

5. lms.txt / lmsfull.txt Files

• lms.txt – simple markdown list of key pages; add to robots.txt (even if redundant).
• lmsfull.txt – full documentation dump for sites with extensive docs; yields 5‑10× more pickups on platforms like Cursor and Winsurf.
• Example robots.txt entry:

  User-agent: *
  Allow: /lms.txt
  Allow: /lmsfull.txt

6. Freshness Management

• Expect a 1‑2 month decay in citation volume after publication.
• Schedule regular updates (e.g., quarterly refreshes) for high‑competition topics (CRMs, credit cards).
• Evergreen CLPs (Commercial Landing Pages) – problem‑oriented pages that map each use‑case to a dedicated URL stay visible longer.

7. Content Type Prioritization

• Listicles & comparison tables → dominate citations (≈33 %).
• Blogs & opinion pieces → low citation share (<10 %).
• Actionable micro‑apps (e.g., “generate a PDF invoice”) can win high‑intent clicks when paired with a well‑structured landing page.

8. Platform‑Specific Insights

• Perplexity: higher CTR, lower traffic volume → high‑quality leads.
• ChatGPT: massive scrape volume, lower CTR but still valuable for brand awareness.
• Google AI Overviews (future): anticipate similar patterns; early adoption of the tactics above will pay off.

💡 Related: How to Build a Page AI Will Love (AEO Checklist)

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Industry Insights & Outlook

• Funding & Market Momentum: Profound closed a Series A led by Kleiner Perkins, NVIDIA Ventures, and Coastal Ventures (June 2025). No additional round has been announced as of February 2026.
• LLM Evolution: Expect a split between lightweight language models (front‑end) and heavy knowledge bases (back‑end). Local LLMs on devices may become common, with search handled by a separate “knowledge engine.”
• Economic Model: Major players (Google, OpenAI) are pushing toward token‑free search to lock in traffic, mirroring Amazon’s loss‑leader strategy.
• Advertising in AI Search: Early experiments (e.g., Perplexity’s sponsored follow‑up queries) hint at a future where ad slots exist inside answer‑engine responses, potentially boosting CTRs dramatically.
• Content Decay & Competition: In fast‑moving categories (headphones, CRMs) the “top‑citation” share can shift weekly; in niche topics the decay is slower.

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Profound Product Roadmap (as of 2025‑06‑30)

Feature	Status	Value Proposition
Conversation Explorer	Live	Query‑volume insights across ChatGPT, Perplexity, Bing, soon Google AI
Actionable Content Brief Generator	In beta	Turns raw query data into ready‑to‑publish briefs directly in the platform
AI‑Driven Content Creation	Upcoming	Generates optimized copy (including tables, schema markup) without leaving Profound
Enhanced Reporting Dashboard	Planned	Visualizes AI‑search traffic, CTR, conversion, and freshness decay curves
Self‑Serve Tier	Launched ($500 / mo)	Low‑barrier entry for SMBs and mid‑market brands

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Summary

AI‑native search is reshaping how brands get discovered. While the underlying muscles—titles, URLs, schema—remain familiar, the distribution of intent and the retrieval mechanisms of LLMs demand new tactics:

1. Leverage year‑stamp tokens in URLs and titles to capture generative query bias.
2. Structure content for “semantic chunks” that provide concise, data‑rich answers ready for citation.
3. Prioritize listicles, comparison tables, and micro‑apps that align with the dominant citation formats.
4. Deploy lms.txt/lmsfull.txt to make your site crawlable for answer engines.
5. Monitor freshness decay and keep evergreen, problem‑oriented landing pages up‑to‑date.

Profound’s Conversation Explorer and upcoming content‑generation tools give marketers a data‑backed roadmap to optimize for AEO without abandoning traditional SEO foundations. By adopting the tactics above, brands can secure high‑quality AI‑search traffic, improve conversion rates, and future‑proof their digital presence as the search landscape continues to evolve.