By Joseph Mas
Document Type: AI Visibility Field Note
Published: January 16 2026
Classification: Observational Hypothesis
This document is an observational field note modeling behavioral change in search execution patterns.
Scope
This field note documents an observation and hypothesis based on current system behavior and publicly reported trends. It models behavioral transition only. It does not address revenue, advertising dynamics, or platform specific monetization.
Observation
Search behavior is increasingly non monolithic. It is bifurcating into two distinct execution layers. The first layer is traditional query based search. The second layer is agentic task fulfillment, where retrieval is embedded inside execution rather than serving as the final interaction.
https://www.a16z.com/ai-agents-are-the-new-apps
https://www.sequoiacap.com/article/ai-agents
This transition allows classic search engine market share metrics to remain relatively stable while the behavioral share of classic search declines. A dominant engine can remain dominant inside a category that is itself contracting.
https://www.gartner.com/en/articles/what-is-agentic-ai
Context
Three years ago, classic query based search remained the dominant interface for intentional information seeking. Browsing and clicking were the default mechanisms for discovery.
https://www.pewresearch.org/internet
https://www.statcounter.com/search-engine-market-share
As conversational assistants emerged, low friction informational queries began moving upstream. Traditional search volume appeared broadly stable, while evidence of intent leakage began to surface.
https://www.microsoft.com/en-us/worklab/work-trend-index
https://www.bain.com/insights/how-generative-ai-is-changing-search
Model
Define S as the share of information seeking routed through classic query based search interfaces.
Define A as the share routed through agentic systems that retrieve, synthesize, decide, and execute.
As A increases, S is expected to decline, even when search engine market share comparisons between vendors appear largely unchanged.
https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai
Phase Progression
Pre agentic baseline.
Most informational intent routes through classic search.
Early AI phase.
Assistants absorb simple informational queries. Search remains common, but early intent leakage begins.
Seeding phase.
Assistants handle synthesis, comparison, and planning. Enterprise discovery shows increasing adoption of internal AI search systems. Classic search loses centrality for some work related tasks.
https://www.gartner.com/en/newsroom/press-releases/2023-05-25-gartner-predicts-search-engine-volume-will-drop-25-percent-by-2026-due-to-ai-chatbots
Acceleration phase.
Agents convert queries into tasks. Retrieval becomes API mediated. Browsing becomes optional in many workflows. Classic search trends toward a fallback rather than a default interaction model.
https://www.sequoiacap.com/article/ai-agents
https://www.a16z.com/ai-agents-are-the-new-apps
Implication
If agentic systems continue to accelerate, classic search may become a specialized tool used primarily for validation, edge cases, and deep investigation. Everyday discovery and execution increasingly move upstream into agent driven systems. Search engines may retain dominance within their category while the category itself becomes more infrastructural than behavioral.
https://www.forrester.com/blogs/the-future-of-search-is-not-a-search-box
Relation to AI Visibility Canonical Definition
The behavioral transition described in this field note is a primary driver for the emergence of AI Visibility as a distinct discipline. As retrieval and attribution move upstream into agentic systems, visibility depends less on ranking and more on ingestion, provenance, and continuity as defined in the canonical AI Visibility theorem.
https://josephmas.com/ai-visibility-theorems/the-dendritic-index-canonical-definition-for-ai-visibility/