Over the past two months, I've been telling you about the Source Signal Stack: a framework for understanding which content signals LLMs trust when they decide who to cite.

Today, I want to dig into the prerequisite beneath all four layers that determines whether the content you produce through any of them actually becomes citable, or just gets absorbed into the model's general knowledge without attribution.

Here, you'll find a condensed synthesis of published research; citation data and methodologies belong to the original researchers cited throughout; this is not original research.

The strategic implication this brief draws: LinkedIn long-form articles and newsletters now function as AI search assets that AI models cite, in addition to being SEO assets that rank on Google.

If you’re going to invest in AEO, you need to be able to prove it out. Let’s get to the elephant in the room when it comes to AI search/AEO conversations: Are these efforts actually bringing your company leads and sales?

Find out how leveraging B2B creators on LinkedIn can help improve your AI search visibility when internal employees aren’t available to help with these efforts.

If internal expertise is limited, you can supplement with specialized external B2B creators, influencers, and thought leaders in your niche for Layer 3 of the Source Signal Stack. (Reader, did you know "LinkedIn influencer" was a thing?)

These are people who already publish consistently, are in front of your ideal customers on LinkedIn, already have a defensible POV in your category, and have a track record of generating the kind of independent, cross-verifiable signal the Source Signal Stack was built to reward.

The Source Signal Stack is a four-layer map of the source signals (aka types of data LLMs cross-verify) when deciding which content to cite on a topic. Read the list top to bottom, and you’ll see there's a principle hiding in it that most B2B marketers have not yet fully articulated: the further a source signal originates from brand control, the more weight LLMs give it.

Every time an employee publishes original content under their own name, in their own voice, on LinkedIn, they're creating a source signal that AI can find, verify, and reference independently of your brand. That post, if it's substantive, specific, and consistent with their named area of expertise, has a 95% better chance of being cited by AI than a reshare of your company's content would. It's social media and AEO strategy.

Your SME interview isn't just you gathering quotes from third-party experts to improve the quality and trustworthiness of the content, but it’s also a source of entity signals, which are an important piece of how LLMs decide which content to cite.

B2B content monoculture is here, and it's a direct result of an entire industry running the same AEO playbook at the same time. Here’s how you can differentiate your content in a sea of samness.

The good news is that closing the gap between ranking well and getting cited doesn't require a full content overhaul. You just need to know exactly where you stand, which pages are closest to crossing over, and what's keeping them from getting cited.

LLMs prioritize original expert insights—but AI can't yet conduct interviews the way humans can. Here's how to prep, run, and extract gold from every SME call.

As a fractional head of content for enterprise SaaS clients, I'm running real content operations every week. That means tight timelines, high expectations, and no room for tools that look good in a demo but fall apart in day-to-day use.

The shift from keyword-level to topic-level thinking isn't just a tactical adjustment; it's a philosophical reorientation of how you build content authority. The teams that make that shift now are the ones who'll be visible in AI search six months from now.