Your Content Isn’t the Answer Anymore
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LLM Impact: Content is increasingly consumed by LLMs rather than human readers, affecting traffic and engagement.
Content Shift: Marketing teams must adapt to a digital landscape where LLMs synthesize content, making traditional strategies less effective.
Personalization Challenge: LLMs tailor responses based on user context, complicating efforts to ensure brand visibility and communication.
Brand Distinction: Specific viewpoints and brand identity may be lost in LLM synthesis, creating a challenge for brand recognition.
Content Strategy: Focus on clarity and structure, ensuring content aligns with buyer questions to improve discoverability.
Your LLM content strategy is being read by someone you didn't write it for.
Now, models are synthesizing your pages into a paraphrased answer your buyer reads before they ever find your website.
So if your Q2 traffic numbers look strange, this is why. Andrea Saez, Head of Product Marketing at Turtl and co-author of The Product Momentum Gap, saw this shift early.
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"People just aren't going to websites anymore," she says "they're querying an LLM and they're getting a list of vendors."
Most marketing teams are still writing for the human reader. Except, the reader has already moved on.
Audience habits changed
The amount of content your team is producing likely isn't decreasing, but the problem now is who's on the receiving end.
For years, you've been structuring content for a human reader navigating Google. You wrote articles or features to be easily skimmed, carry a strong narrative arc, and offer specific details that makes an abstract concept easier to understand. The formula worked.
Now, Saez argues, most content is being processed by LLMs before a buyer ever reaches it. They're getting a synthesized answer shaped by how the model interpreted their query. And, now, your page may never load for them at all.
Content is actually becoming longer and longer because you have to try and put all of this stuff in," she said. "But no one's actually reading it. The content we're creating is really just for the LLM to be able to extract it.
Between anecdotes, clarity, and narrative structure, articles are designed to help something land for a human reader. And none of it's optimized for machine ingestion.
The content that does well in LLMs are complete, well-structured, and directly relevantt to the query. Your buyer, increasingly, gets whatever the model decides to surface.
There's no ranking in an LLM
Many have inquired on how you get your content to rank in a LLM. The truth is, we don't know.
People think that you can rank in an LLM, but you can't," Saez said. "There's no ranking. The best you can do is try to get picked up as much as possible.
Search had a map. Keywords, backlinks, domain authority. Sure, it was messy, but followable. You could watch rankings shift in response to decisions you made.
But models don't offer that, and your LLM content strategy needs to account for this.
You can run the same prompt fifteen times and appear in twelve of them. There's no reliable signal explaining why you showed up or why you didn't.
What you can do is make sure your content answers the questions your buyers are actually asking, as precisely as possible. The catch is you don't get to see the question.
"There's no way of actually knowing what question they're asking," Saez said.
You're optimizing for a query you never see, from a buyer whose behavior you can no longer track the way you used to. Even if your team hasn't started wrestling with that yet, the teams around you already have.
The personalization layer makes it harder
There's a dimension to this that gets underplayed in most AEO conversations.
LLMs go beyond responding to queries, now it's tailored to respond specifically to the person asking. The more a model knows about the inquirer, the better able it can tailor the response to fit that person's context.
Two buyers asking the same question about your product category may get two entirely different responses—neither of which you can see or anticipate.
Saez shares a personal anecdote to explain how this is working in real time. She asked Claude to recommend restaurants in Amsterdam and got results matched precisely to her preferences because the model knew what these were. Her partner, asking the same question, would have gotten something different.
Now put that into a B2B context," she said. "If I'm the VP of marketing, I'm going to an LLM and going, 'Hey, I have this particular problem.' How is it then personalizing the results for them? And what can we as marketers do to ensure that we're getting into those results? I have no idea.
Saez has been in this longer than most. If she doesn't have a clean answer, it's because one doesn't exist yet. Humility and active experimentation are the only honest operating modes right now, and the window to start is narrowing.
What gets filtered out
The harder question is what LLM synthesis actually strips away.
The specificity of your point of view, the things that make your brand distinct rather than interchangeable. And those don't survive compression particularly well. What survives is the answer to the query. Your brand either gets extracted as a relevant response, or it doesn't appear.
As an author, that makes me really sad," Saez said. "It's missing the empathy. It's missing the human flavor.
If the machine is now the first reader, what does your buyer experience when they eventually encounter your brand directly? For many of them, they won't. Most people will take the synthesized answer, trust it, and make a decision. The work behind it stays invisible.
This is a brand problem, not just a content problem. And it compounds the longer you wait to address it.
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Where to start
There's no clean playbook for CMOs. And Saez was pointed in her response, noting that false certainty doesn't serve anyone in this era of LLM content parsing.
What's clear is that content written around what your company wants to say, rather than what your buyer is trying to learn, is getting filtered out.
Structure now does more work than style. Direct answers, clearly framed and close to the top of the page, are what LLMs can parse and surface—akin to how ATS systems match keywords to source applicants.
And testing your own discoverability across different prompts and platforms, while imprecise, is currently some of the most honest signal available.
Hold onto the craft anyway. The buyers who find their way to your crafted content, who read rather than just take the synthesized answer, are probably your best buyers. Saez still writes for them, even as she rebuilds her broader strategy for a world that's already moved past the page.
Most teams haven't fully named this shift yet. The ones that have are already several steps ahead.
What's Next?
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