{"podcast":{"title":"Answer Engine Optimization (AEO): The AI Search Podcast","slug":"answer-engine-optimization-aeo-the-ai-search-podcast-7756998","podcast_index_feed_id":7756998,"rss_url":"https://feeds.transistor.fm/aeo-engine-ai-search-show","website_url":"https://aeoengine.ai","image_url":"https://img.transistorcdn.com/zSuBki8a-UnBBIDVwHrDe-F3ZlgSGxtUolU9c1LN8sM/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS8yNGE4/MjdjYmFkYTNjNDBj/NWU4ZTFiMzdkZTI5/YWVhYy5wbmc.jpg","author":"AEO Engine","episode_count":112,"summary":"Answer Engine Optimization (AEO) is how your brand gets cited, recommended, and surfaced inside ChatGPT, Perplexity, Google AI Overviews, and Claude. This is the daily podcast for marketers, founders, and SEOs who want their brand to be the answer AI engines give. Each episode breaks down a new AEO tactic, a real algorithm change, or a brand that just won (or lost) visibility inside AI search. Topics include: how ChatGPT decides which brands to recommend, how Perplexity chooses its sources, how Google AI Overviews differ from traditional SERPs, how to structure content for LLM citation, schema strategies for answer engines, and the emerging field of Generative Engine Optimization (GEO). Brought to you by AEO Engine — the platform brands use to monitor, measure, and grow their AI search visibility. Whether you're a B2B marketer, DTC founder, or in-house SEO, this podcast turns the daily chaos of AI search into a concrete playbook you can execute on. New episode every morning. Transcripts on every episode. Subscribe to stay ahead of how AI engines rank and recommend brands.","last_synced_at":"2026-06-17T08:18:55.452836+00:00","page_url":"https://stenobird.com/podcast/answer-engine-optimization-aeo-the-ai-search-podcast-7756998"},"episode":{"title":"Glean's Waldo: The Agentic Search Model Making AI Faster and Cheaper","slug":"glean-s-waldo-the-agentic-search-model-making-ai-faster-and-cheaper","published_at":"2026-05-02T12:33:44+00:00","page_url":"https://stenobird.com/podcast/answer-engine-optimization-aeo-the-ai-search-podcast-7756998/glean-s-waldo-the-agentic-search-model-making-ai-faster-and-cheaper","show_page_url":"https://stenobird.com/podcast/answer-engine-optimization-aeo-the-ai-search-podcast-7756998","url":"https://share.transistor.fm/s/52bff747","audio_url":"https://media.transistor.fm/52bff747/413dfcbb.mp3","summary":"Glean's Waldo introduces an agentic search model that uses reinforcement learning to optimize the retrieval process. By acting as a specialized intermediary, it significantly reduces latency and token consumption without sacrificing final answer quality.","meta_description":"Explore how Glean's Waldo uses agentic search and reinforcement learning to cut AI latency by 50% and token usage by 25%.","key_points":["Main idea: Waldo functions as a specialized agent that performs query decomposition and tool selection before hitting frontier models","Efficiency gain: The model achieves 50% lower latency and 25% fewer tokens consumed through optimized retrieval","Technical mechanism: The system uses a planning loop of query decomposition, iterative search, and evaluation rather than a single lookup","Strategic shift: The rise of agentic search necessitates a move from traditional SEO to Answer Engine Optimization (AEO) to ensure brand discoverability","Failure mode: Relying on monolithic models for complex enterprise queries leads to unsustainable costs and slow response times"],"chapters":[{"start_ms":0,"title":"Introduction to Waldo","summary":"An introduction to Glean's new agentic search model and its impact on enterprise AI efficiency."},{"start_ms":60000,"title":"The Problem with Massive Models","summary":"Discussing the latency and token costs associated with sending every query directly to frontier LLMs."},{"start_ms":120000,"title":"Efficiency Without Quality Loss","summary":"How Waldo uses a curated set of evidence to maintain high-quality answers while reducing compute."},{"start_ms":180000,"title":"The Agentic Reasoning Loop","summary":"A deep dive into query decomposition, tool selection, and the iterative search process."},{"start_ms":240000,"title":"The Future of Enterprise AI","summary":"Analyzing the shift from monolithic models to orchestrated systems of specialized agents."},{"start_ms":300000,"title":"Optimizing for Agentic Discovery","summary":"How brands must adapt their content structure to be found by intermediate search agents."},{"start_ms":360000,"title":"Conclusion and Takeaways","summary":"Final thoughts on the reality of agentic search models and the changing landscape of AI information consumption."}],"topics":["Agentic Search","Glean Waldo","Reinforcement Learning","Enterprise AI","LLM Efficiency","Answer Engine Optimization","Query Decomposition","Retrieval Augmented Generation"],"duration_seconds":394,"processing_state":"processed","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/answer-engine-optimization-aeo-the-ai-search-podcast-7756998/episodes/glean-s-waldo-the-agentic-search-model-making-ai-faster-and-cheaper/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/answer-engine-optimization-aeo-the-ai-search-podcast-7756998/glean-s-waldo-the-agentic-search-model-making-ai-faster-and-cheaper.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}