From structured web to agentic discovery.
A chronological record of the infrastructure, products and research that changed how machines understand and surface information.
Schema.org establishes a shared structured-data vocabulary
Structured data became a durable layer for machine understanding and remains relevant to search, product discovery, entities and AI-facing content systems.
Google launches the Knowledge Graph: “things, not strings”
Modern AI visibility depends heavily on consistent entity identity and relationships across the web.
Google introduces Featured Snippets
This is an important precursor to AEO: content structure and extractability became a visibility objective distinct from ordinary ranking.
Google introduces the conversational Google Assistant
Voice and assistant interfaces strengthened the need to become the selected answer, a core idea later associated with AEO.
BERT improves Google Search’s language understanding
Semantic clarity and contextual relevance became more important than literal keyword matching alone.
Retrieval-Augmented Generation (RAG) is formalized in research
RAG is one of the core technical patterns behind modern AI search, source retrieval and citation behavior.
OpenAI WebGPT demonstrates browser-based answering with citations
The retrieve-read-cite pattern closely anticipates later AI answer engines and modern citation optimization concerns.
Perplexity launches its answer engine
It helped establish the answer-engine model that later drove GEO, citation tracking and AI visibility measurement.
Microsoft launches the new AI-powered Bing
Generative answers and citations moved into a major search engine, accelerating the need to understand visibility beyond traditional rankings.
Google announces Search Generative Experience (SGE)
The dominant search platform formally entered generative search, making AI-mediated source selection a core visibility issue.
ChatGPT web browsing begins rolling out in beta
Website discoverability became directly relevant to ChatGPT answers and later evolved into a dedicated search product.
Research paper formally introduces Generative Engine Optimization (GEO)
GEO became a named research problem with explicit visibility metrics rather than only an informal marketing practice.
Google launches AI Overviews broadly in U.S. Search
AI inclusion, source selection and click behavior became material concerns for mainstream SEO and GEO strategies.
The llms.txt convention is proposed
It reflects growing demand for content representations optimized for machine readers, although it is not an established web standard.
Cloudflare launches AI Audit for crawler visibility and control
AI discoverability increasingly depends on deliberate crawler policy, not only content and ranking signals.
OpenAI launches ChatGPT Search
ChatGPT became a direct discovery channel where crawlability, source selection and citation visibility matter.
Google introduces AI Mode and describes query fan-out
Visibility is no longer tied only to the user’s literal query; sources can be discovered through hidden subqueries and related retrieval paths.
Anthropic launches web search on the Claude API
AI discoverability extends beyond consumer search interfaces into embedded enterprise and developer workflows.
Google expands AI Mode and Deep Search using query fan-out
Topical completeness, subtopic relevance and retrieval across multiple intents become central to AI search visibility.
ChatGPT launches Shopping Research
Product visibility now includes machine-readable product data, reliable attributes, merchant data and recommendation eligibility.
Google publishes official guidance for generative AI Search visibility
Practitioners gained first-party guidance separating verified requirements from speculative AEO/GEO tactics.
Cloudflare separates AI traffic into Search, Agent, and Training
AI visibility strategy must distinguish discovery/search access from model training and real-time agent access.
Cloudflare expands BotBase for bot and agent transparency
Verified crawler identity can affect whether publishers allow AI systems to retrieve content and therefore whether content remains discoverable.
Perplexity introduces Q2D-Web for large-scale retrieval evaluation in agentic RAG
The visible user prompt is not necessarily the query a source competes for. AI visibility therefore depends on several distinct stages: query reformulation, first-stage retrieval, ranking, evidence selection, and eventual citation.
Cloudflare’s new Search / Agent / Training crawler defaults take effect
A publisher’s AI policy can now directly influence discoverability, agent access and potentially conventional search access when crawler purposes overlap.