⭐ The State of AEO 12/4 – The Retrieval Architecture
[State of AEO Call Recording] [State of AEO Slide Deck] [State of AEO Access and Retrieval Checklist] This session drilled down into the mechanics of Indexability, specifically focusing on how we transform our websites from human destinations into efficient data sources for silicon intelligence. Julian emphasized that before an AI can recommend you, it must be able to "afford" to retrieve your data. We explored the technical gatekeepers (Robots.txt), the future of AI documentation (LLMs.txt), and the critical concept of "Cost of Retrieval." 1. 📉 Reducing the "Cost of Retrieval" We started with a fundamental shift in how we view site performance. AI agents prioritize energy and speed. - The Inverted Pyramid: In the past, we buried conclusions to keep humans reading. For AEO, the key takeaways and data points must be at the very top. If AI has to dig through "fluff" to find the answer, the cost of retrieval is too high, and it will move to a cheaper (competitor’s) source. - Speed is Visibility: A slow site isn't just bad UX; it’s an opaque wall to a crawler. We touched on Core Web Vitals and image optimization (WebP formats) not just for loading times, but to ensure the raw HTML is served instantly to the bot. 2. 🚪 The Bouncer: Robots.txt & The Amazon Case Study Access is binary: You either let them in, or you don't. - The Amazon Experiment: Julian showcased a live example of why access matters. When asked "Who are the authors of the AEO Blueprint?" via a link to Amazon: - The Lesson: You might have the best content in the world, but if your robots.txt file is accidentally acting as a bouncer against Perplexity or GPTBot, the AI is forced to guess (and often lies) about your brand.