The biggest misconception marketers have about how AI models surface content
episode
Voices of Search // A Search Engine Optimization (SEO) & Content Marketing Podcast
4 min
2 speakers
6 chapters
transcribed 1 month ago
Transcript
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Transcript generated automatically by AI and may contain errors.
What is the overall premise of the episode and who are the hosts?
The Voices of Search Podcast is a proud member of the I Hear Everything Podcast Network. Looking to launch or scale your podcast? I Hear Everything delivers podcast production, growth, and monetization solutions that transform your words into profit. Ready to give your brand a voice? Then visit IHear Everything.com. Welcome to the Voices of Search Podcast in IHear Everything presentation. introduction. In this podcast, we'll share the news, knowledge, and strategies you need to navigate the ever-changing world of SEO. Ready to expedite your company's organic growth efforts? Sit back, relax, and get ready for your daily dose of search engine optimization wisdom. Here's today's host of the Voices of Search podcast, Tyson Stockton.
My name's Tyson, and joining me today is Kristen Tinsky, SVP of Creative and co-founder at Fractal.
Why do marketers misunderstand how AI models surface content?
What's the biggest misconception that you're seeing with marketers about how these AI systems are surfacing content? Like what's in your mind the biggest misconception with where we're at in search and how something is getting surfaced.
I I think anyone saying Anything about understanding truly what these models are doing is is lying. Um and tools that try to figure out things that are black box are never going to work well. So like uh checkers for AI text are never going to work that well. Uh there will always be a really high false positive and false negative rate.
How do black‑box AI detection tools lead to high false‑positive rates?
I think people really need to consider the drawbacks of the current models and what they're actually capable of versus what they're not. And I think there are there are misconceptions on both sides. I think a lot of people don't realize what they're actually capable of. And I think there are a lot of people that think they're more capable of certain things than they actually are.
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What should marketers know about the limitations of current AI models?
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How does a brand’s presence in training data affect AI visibility?
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So like understanding the deficiencies and the capabilities both are really important. Um, and that takes exploration and working with them consistently. Yeah. I mean other other things like like visibility within an an AI or w across different AIs or even within AI search, I think like that that sort of thing is also super inaccurate. Um I'm not even sure like how valuable it is to really even do it. Uh, I I think you can get like a generalized idea of like you're maybe invisible to them.
What future AI advancements could enable real reasoning inspection?
But if you're like a brand that's existed within their training data sets, you're not gonna be invisible to them. It's a weird situation that we're in right now because these AIs are black box and and because there's really like even if even if the model providers wanted to, they couldn't give us deep insight into to why a certain answer was given.
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Chapters
6 chapters
1
What is the overall premise of the episode and who are the hosts?
0:00–0:52
2
Why do marketers misunderstand how AI models surface content?
0:52–1:39
3
How do black‑box AI detection tools lead to high false‑positive rates?
1:39–1:59
4
What should marketers know about the limitations of current AI models?
1:59–3:03
5
How does a brand’s presence in training data affect AI visibility?
3:03–3:52
6
What future AI advancements could enable real reasoning inspection?
3:52–4:24