Kristin Tynski

speaker

493 appearances 3 recordings 1 series first heard Mar 2026 last heard 26 Mar

Kristin Tynski’s voice in public audio — every appearance, attributed to the second.

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recordings per month · last 12 months

Recordings per month over the last 12 months — 3 in all, peaking in Mar 2026 with 3.

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Voices of Search // A Search Engine Optimization (SEO) & Content Marketing Podcast · Should review platforms be worried about their long-term relevance? · 26 Mar 2026 podcast

I actually think they become much more useful than they currently are.

Um, and it's it's for that same reason that it's it's about understanding not just like what a brand is good at, but what they don't do or what they can't do or who they don't serve.

Um so instead of like a really rough generic three out of five stars, you understand instead it's five out of five for this part of the consumer base and one out of five for this other part.

So everyone that got stuck in the three out of five middle because they

were great for some and horrible for others, now can be the five star thing that they were always meant to be because they're serving their real audience.

I think, yeah, reviews become much more important with the caveat that they need to be trusted.

So like this again becomes, I think, maybe a decision of the model creators where they're deciding what they use in their training sets, or maybe even AIs dis that are deciding whether or not to include things in their initial training sets.

Or developed over time a the heuristics of a model to understand whether or not something is trustworthy or not.

So that that could go much deeper.

But in general I think it'll be about deciding what review sites should be included in the initial training sets that create the models.

Voices of Search // A Search Engine Optimization (SEO) & Content Marketing Podcast · The biggest misconception marketers have about how AI models surface content · 25 Mar 2026 podcast

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.

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.

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