Podcast Episode Summary: Voices of Search
Episode Title: Invest more resources in optimizing for traditional search or non search channels to show up in AI?
Release Date: August 21, 2025
Host: Jordan Cooney
Featured Guest: Sean (from Apollo)
Episode Overview
This episode tackles a critical question for modern marketers and SEOs: With the rapid evolution of AI and generative search—and the emergence of channels feeding AI models—should companies focus their resources on optimizing traditional search or on non-search channels to gain visibility in AI-generated results? Jordan Cooney hosts a concise but pointed discussion with Sean from Apollo, exploring the underpinnings of how search data flows into large language models (LLMs) powering AI-driven interfaces.
Key Discussion Points & Insights
The Resource Allocation Dilemma
[00:43]
- Jordan (Host): Opens with a fundamental question—should companies invest more in optimizing for traditional search or non-search channels to increase AI visibility?
Sean’s Perspective: Prioritize Traditional Search
[00:55-01:13]
-
Sean: Advocates for allocating more resources to traditional search channels.
“Traditional search channels? Absolutely.”
—Sean, [00:55] -
Reasoning:
-
Traditional search results fuel large language models (LLMs). In Sean's view, it's an "order of operations" issue:
“They fuel LLMs. So to me, it’s more order of operations. ... I think that if they're pulling from search results, I want my search results to be as—What did Anthony Bourdain say? You could be a really good cook, but if you have shitty ingredients, you’re gonna be a shitty cook. ... I think you need really, really good ingredients." —Sean, [01:07–01:27]
-
Analogy That Resonates: The Bourdain Principle
[01:13-01:35]
- Sean uses a culinary analogy to explain: LLMs and AI rely on the quality of available digital content ("ingredients"). Focusing on search visibility ensures those foundational “ingredients” are strong.
-
Many companies skip foundational work, seeking visibility for its own sake rather than improving content quality.
-
The analogy drives home the risk of optimizing for new channels while neglecting the quality feeding AI systems.
“I think you need really, really good ingredients. And I don’t think a lot of people have them. I think they're trying to optimize for visibility without focusing on their ingredients.” —Sean, [01:27–01:38]
-
Key Takeaways
[01:38-01:45]
-
Focusing on traditional SEO ensures your content is among the primary sources AI models reference.
-
Non-search channels may matter, but foundational SEO work is prioritized as the starting point for lasting visibility.
“You got to really optimize for traditional.”
—Sean, [01:38–01:45]
Notable Quotes
-
Sean (Apollo):
- “Traditional search channels? Absolutely.” [00:55]
- “If they’re pulling from search results, I want my search results to be as ... What did Anthony Bourdain say? ... If you have shitty ingredients, you’re gonna be a shitty cook.” [01:13–01:22]
- “You need really, really good ingredients. ... They're trying to optimize for visibility without focusing on their ingredients.” [01:27–01:38]
-
Jordan Cooney (Host):
- “That was well said, Sean. Well said.” [01:45]
Important Timestamps
- 00:43: Main topic introduced—where to invest SEO resources for future AI visibility.
- 00:55–01:45: Sean’s reasoning and analogy for prioritizing traditional SEO channels.
- 01:45–02:17: Episode wrap-up and closing remarks.
Summary & Actionable Insight
- Core Recommendation:
Invest in traditional SEO first—the quality and presence of your content in search results fundamentally affect your visibility to AI systems leveraging those same results as "ingredients.” - Strategic Implication:
Before diverting resources to new or unproven channels designed for AI, ensure your traditional SEO is solid. Quality, not just quantity or novelty, drives long-term visibility in both search and AI ecosystems.
This episode delivers a simple but pivotal message for marketers and SEO strategists: Build your content foundation well—the rest of your AI-era visibility depends on it.
