Why the Next Era of Search Rewards Depth Over Reach

In this week’s episode of Voices of Search, we spoke with Kristen Tinsky, SVP of Creative and co-founder at Fractl, a content marketing agency that has run over 5,000 campaigns and is currently developing more than 25 AI marketing agents. Kristen has spent the last several years at the intersection of content strategy and AI, and her perspective on where search is heading is more technically specific than most of what’s circulating in the industry right now.

Today, she broke down what a post-transformer world actually looks like, why being well-defined will matter more than being well-known, and how AI agents are putting investigative content within reach of any brand willing to pursue it.

Key Takeaways From This Episode:

• Transformer-based AI is hitting its limits. The next generation of models will learn continuously, making truly hyperpersonalized search a near-term reality.
• In a hyperpersonalized search world, being well-defined matters more than being well-known. Brands that try to serve everyone risk becoming invisible.
• AI visibility tracking tools are largely inaccurate. These are black box systems and anyone claiming to fully understand what drives their outputs is overstating what’s knowable.
• Link building as a standalone tactic is effectively obsolete. Authority now comes from broad, contextually relevant coverage across trusted sources.
• AI agents are enabling a new era of data journalism for brands. What once took weeks can now be done in hours.
• Review platforms are becoming more important, not less. The granularity of sentiment in reviews will help AI models match brands to the audiences they actually serve best.

The Post-Transformer World and What It Means for Search

To understand where Kristen is pointing, it helps to understand what she means by transformers and why their limitations matter.

Current AI models are built on transformer architecture, essentially a giant matrix where every parameter interacts with every other parameter during training. The problems with this are twofold:

• They’re extraordinarily compute-intensive and expensive to update
• They don’t learn as you use them. They’re trained once, then deployed, and every update requires retraining from scratch at enormous cost

The next generation of models will solve this through continual learning. Rather than being trained and then frozen, they’ll integrate new information in real time, the way human memory works. 

“You’ll have an individual AI that is growing with an individual person,” Kristen explained. “The model is actually integrating information into its system in a continually learning sort of way.” They’ll also be small enough to run on personal devices rather than massive server farms, resulting in AI assistants that grow with individual users over time.

For search, this changes everything. “Any search that’s done will always inherently be hyper-specific to the person that’s doing the searching,” Kristen said. There won’t be a universal ranking system. There will be individualized answers, shaped by everything a person’s AI has learned about them.

What This Means for Brands That Try to Be Everything to Everyone

The strategic implication of hyperpersonalized search is one that many brands will find uncomfortable: specificity wins, breadth loses.

In today’s search environment, there’s a winner-take-all dynamic at the top of results. But in a hyperpersonalized world, the long tail gets served much more frequently because the right answer for a specific person might be something highly niche that only one source has ever addressed properly.

For brands, the goal shifts from capturing broad audiences to becoming what Kristen calls “the canonical best answer” for a specific persona. 

“If you try and do too much and try and serve too many audiences but half-heartedly do each one, then the models are going to think you’re this non-specific thing,” Kristen said. 

A brand that halfway serves five different audiences will be mathematically less visible than one that deeply, specifically serves one. The AI builds a representation of the brand in its latent space, a kind of mathematical shape, and matches it against the shape of what a given person needs. The closer those shapes align, the more likely the brand is to get recommended.

Three things accelerate irrelevance in this world:

• Fluff language and superlatives. “The models do not care about this,” Kristen said. “They will assess you on their own.”
• Vague or inconsistent brand positioning that makes it hard for AI to understand what you actually do
• Trying to serve too many audiences without the depth to serve any of them well

What matters is an accurate, specific, honest representation of what you do well and, crucially, what you don’t.

Authority Has Changed, and Link Building Hasn’t Just Evolved—It’s Been Replaced

One of the cleaner moments in the conversation was Kristen’s take on links and authority. Link building as a tactical discipline, chasing domain authority scores and acquiring individual links, is effectively obsolete in an AI-driven world. These were heuristics designed for search engine crawlers. They’re not how AI models assess credibility.

What does matter is broad coverage: being mentioned across trusted, well-known sources in the context of the service you offer and the customers you serve. 

“If you could get a lot of coverage that mentions your brand within the context of the service that you offer and the customers that you serve, that’s hugely important,” Kristen said. “Getting a single link from one place wouldn’t do the same thing it did ten years ago.”

The shift is from link building to authority building, and authority is now established through the breadth and consistency of how your brand is discussed across the broader information landscape.

Chasing Visibility Metrics Is Itself a Shallow Activity

Kristen was unusually direct on a topic most vendors in this space won’t touch: spending significant time on AI visibility tracking tools is, for most brands, a low-value activity that pulls focus away from the work that actually matters.

The core problem is that current AI models are black boxes. Even the companies that build them can’t fully explain why a particular answer was generated. “Anyone saying anything about understanding truly what these models are doing is lying,” Kristen said. Even if you could get perfect visibility data, it wouldn’t tell you how to improve—because the underlying system is opaque.

The time is better spent going deeper into what actually drives AI relevance: understanding your personas with precision, producing content that genuinely expands the knowledge of your category, and defining your brand in a way that’s specific enough for a model to know exactly who you serve and who you don’t. 

That’s the work that compounds. Visibility reports are just a measure of whether you’ve done it.

Brands as Journalists: The New Content Strategy

The most forward-looking part of the conversation was Kristen’s argument that AI agents are enabling a fundamentally new kind of content strategy, one where any brand can operate like a journalistic outlet producing original, data-driven investigations.

At Fractal, much of the work involves what Kristen describes as data journalism: gathering and analyzing datasets, finding what’s statistically significant and newsworthy, and publishing investigations that add something genuinely new to a topic. Until recently, this was expensive and slow.

To illustrate how dramatically that’s changed, Kristen walked through a project she’d done a year and a half ago: analyzing speech patterns in political figures’ public addresses to explore questions about cognitive decline. The process involved:

• Finding and scraping audio sources with custom code
• Converting audio to text using voice-to-text tools
• Running lexical and statistical analysis across transcripts

The whole thing took two weeks. She recently redid the same project using Claude’s agentic environment. It completed nearly the entire workflow autonomously, going deeper than the original, in a fraction of the time. “Being able to do a highly complex project like that and even go deeper than I had before was just ten times easier,” Kristen said.

“What does that mean for the future of content when you can do an investigation of that depth in a matter of hours versus a matter of weeks?” Kristen asked. 

Her answer: Every brand now has the ability to become a canonical source of knowledge about their industry, their customers, and their category. “In my opinion, every brand could become a journalistic outlet essentially. They could do deep investigations in their industry on absolutely everything they wanted to.”

The One Strategic Shift Worth Making Now

Asked to distill her content strategy advice to a single point, Kristen’s answer was clear: start doing the deep investigations you’ve always wanted to do but thought were out of reach.

“Start thinking about the deep investigations and questions that you’ve always had about your industry, your product, your service, your customer, their needs, their psychology, and how it could be investigated,” she said. “You don’t need to be a coder. You don’t need to be a statistician. You just need to have an idea and to be able to work with an AI to bring it to fruition.”

Ultimately, the brands that will win in an AI-first search world are the ones producing content that tells the truth, defines who they are with precision, and goes deeper into their subject matter than anyone else. 

The tools to do that now exist. The question is whether brands use them to build something that compounds, or keep producing content that a model will eventually learn to discount.

Voices of Search is a daily SEO and content marketing podcast hosted by Jordan Keone and Tyson Stockton. The show delivers actionable strategies and data-driven insights to help marketers navigate the ever-evolving world of search engine optimization and content marketing. New episodes air weekly, covering everything from technical SEO to AI discovery, featuring industry leaders and practitioners sharing real-world frameworks and proven tactics.

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