For the past couple of decades, SEO has been about linear visibility. Your website ranks for more keywords in higher positions, which, in turn, drives more clicks, and has been benchmarked by total opportunities in search (MSV) and ranking comparisons against your competitors.
This model worked well because search operated within a shared reality, and even with the “light touch” personalization Google was making, there was a recognizable, mostly replicable search results page. These benchmarks for success were universally known, repeatable, scalable, and understandable when SEO services were being purchased.
Google’s latest shift toward personal intelligence is further progressing a change we’ve been seeing over the past couple of years with the increasing accessibility and adoption of AI. Even prior to personal intelligence, we’ve seen the results produced by all LLMs vary greatly across users and are rarely repeatable. This is more than just having an AI interface layered on top of search, but is a shift away from shared search results within a shared reality to personal search being the default.
This takes search as we know it away from being “personalized search” to being user-habit based, memory-aware, and shaped by the users’ overall digital footprint, preferences, and experiences.
For users, this is shaping how people are searching and moving away from the notion of “find me information” to “find me a solution.” As search/AI search is becoming more conversational, and journeys are becoming more multimodal, less linear, and users have access to more information than ever before, we’re evolving from the long tail to the infinite tail.
From Long Tail To Infinite Tail
Over the past couple of decades, the way we talk about search has centered on keywords, typically dividing them into short-tail and long-tail queries, where a short-tail search might be something like “cheap holidays” and a long-tail query would be more specific, such as “cheap holidays for families in Europe.” When voice search started gaining traction, we saw a shift toward question-based searches that led to an entire SEO economy built around question-focused content and top-of-funnel, information-led discovery.
Short Tail > Long Tail > Infinite Tail
That model made sense when most searches happened in a single place (the search bar), but today, that is no longer the case because people now search through Google, TikTok, Instagram, social platforms, and LLMs. This means search has become multimodal and multiplatform, extending beyond typed queries into voice, images, video, and conversational prompts, creating user journeys that are fragmented, unpredictable, and far from the clean, linear paths we once mapped out, and what we are entering now is what I call the infinite tail.
In the keyword-only era, users operated within clear boundaries and tried to choose the right words because they understood the system depended on those words. Meanwhile, keyword research tools reflected a finite, measurable set of phrases, making the universe of search terms feel vast yet ultimately countable, something we could quantify and model. This is precisely the foundation the SEO industry was built on.
AI search changes this dynamic by removing many of those constraints and shifting us into natural language interactions, mixed media outputs, and conversational refinement. People no longer feel pressure to compress their intent into carefully engineered phrases and can instead express what they want in whatever way feels natural. This aligns with the principles of information foraging theory that describe users as hunters moving between patches while constantly weighing effort versus reward. When friction drops, exploration increases, and AI lowers that friction dramatically, allowing users to pursue nuance without the same cognitive cost.
As the cost of refinement/additional user effort approaches zero, users assume the model will interpret them correctly and therefore experiment more freely. As personalization deepens, friction reduces even further. AI simultaneously offloads a user’s cognitive effort by framing responses, structuring comparisons, and pulling together information from multiple sources so that users no longer need to open multiple tabs, read several articles, and manually compare options since the system can synthesize and summarize on their behalf.
Keyword Research For The Infinite Tail
If the query space is effectively infinite, keyword research cannot remain a process of building a fixed list and attempting to rank for each term individually.
Traditional keyword research assumed a relatively stable demand set. You identified head terms, expanded into the long-tail, catered to FAQs, grouped them into clusters, and mapped content accordingly. Success meant increasing coverage across that measurable universe.
With the infinite tail, instead of optimizing for a predefined set of keywords, we optimize for intent expansion and intent satisfaction.
Fan-out queries are the expansions an AI system generates as it explores adjacent variations, comparison angles, constraints, and decision factors around a task. A simple question about “quiet beaches in November” can quickly branch into topics such as crowd levels, flight routes, food options, safety, walkability, and budget limits. Your content does not need to rank for every individual phrasing, but it does need to fully support the broader decision space surrounding the task.
Grounding queries serve as the system’s validation layer. These checks pull from trusted sources, structured data, reviews, and corroborating signals to reduce hallucination and risk. If your brand is not firmly grounded through clear entity signals, deep topical coverage, structured information, and credible external validation, it becomes less likely to be chosen when the system needs to justify its answer.
Keyword research now expands in two distinct directions.
Firstly, it shifts from extractive to exploratory, and instead of just collecting phrases, we examine how tasks break down, how user journeys unfold step by step, and where intent naturally branches. We map problems and real use cases, the problems users are trying to solve, not just search terms they’re using as vehicles to get from A (the problem) to B (the solution).
It also becomes much more constrained at the brand level. In a probabilistic ranking model, authority tends to cluster around clearly defined categories. A probabilistic ranking model is one that estimates how likely a piece of content is to satisfy a specific inferred intent, rather than assigning it a fixed position for a single keyword.
Trying to rank for everything, even loosely related, in the pursuit of traffic, weakens your signals. Broad, unfocused coverage erodes your position within any single intent cluster. The strategic move then is to go narrower, not wider.
You then need to define the category where you want to be the default choice, then build dense, interconnected coverage around real-world use cases within that space. Strengthen entity clarity, trust signals, and behavioral reinforcement so that grounding mechanisms consistently recognize you as a reliable authority – and this is where building your brand starts to compound in AI search.
In practical terms, this means moving away from asking how many keywords you can rank for, and instead, focusing on how completely you solve a defined class of problems, and how consistently the system associates your brand with that solution space. You then market like hell to your audience and gain leverage in the next wave of personalized search.
In the infinite tail, traffic growth no longer comes from capturing small keyword variations. It comes from increasing the likelihood that your brand is selected across countless fan-out paths within a clearly defined domain of expertise.
More Resources:
- Query Fan-Out Technique in AI Mode: New Details From Google
- The CMO & SEO: Staying Ahead Of The Multi-AI Search Platform Shift (Part 2)
- SEO In The Age Of AI
Featured Image: Roman Samborskyi/Shutterstock
