Podcast Episode

Marketing budgets have dropped to less than 8% of company revenue, roughly 18% below where they sat four years ago, and the AI line item inside those smaller budgets keeps climbing. Put those two numbers next to each other and one move looks obvious: hand the search work to AI, stop paying for the people, and bank the difference.

Many AI search plans rest on a comforting assumption: teach the model what you do, describe yourself clearly enough, and the recommendations will follow. It feels right, and it's shaping how a lot of marketing budgets get spent. In this episode, we share what happened when we tested this assumption.

There's a version of your brand that lives only inside AI answers, and you probably didn't write it. When a buyer asks ChatGPT or Gemini about your company, the description they get back is assembled from sources scattered across the web, and a lot of them are places you've never touched. Sometimes that description flatters you. Sometimes it's wrong in a way that quietly costs you the deal before anyone on your team picks up the phone.

Most marketers treat AI search as a black box that either likes their brand or doesn't. It isn't. Behind every answer an AI gives about you sit two very different machines, one that talks and one that knows, and they behave in ways you can actually reason about once you've seen how each was built. Learn more about the history of AI search in this week's episode.

Most AI-driven visits land in your analytics as direct traffic with no source label, which means your current reporting can't tell you whether your brand is showing up in ChatGPT, Perplexity, or Google's AI answers. This episode breaks down why that gap exists and shares how to build the measurement layer that actually captures it.

The buyers who arrive at your site from AI are further along in their decision than almost any other traffic source. Getting in front of them takes a different kind of work than ranking in traditional search, and that work is cheaper to do now than it's likely to be later.

In this debut episode of The Search Signal, Michael Transon cuts through the acronym soup to show what the data actually says, where this confusion is coming from, and what it means for how you should be allocating your search marketing budget right now.