Brands keep asking the same questions about AI search: what works, what doesn't, and what the data actually says. We answer them here, with original research grounded in peer-reviewed studies, enterprise benchmarks, and primary data from our own pipeline. Every claim links back to a source in our methodology.
The terms used across the info.link research library, defined once.
The practice of optimising content to surface in AI-generated answers from systems like ChatGPT, Perplexity, and Google AI Overviews.
Coined in a 2023 research paper from Princeton, Georgia Tech, and the Allen Institute for AI, which showed that adding citations, quotations, and statistics to a page can raise its visibility in AI answers by up to 41%. Used most widely in 2025–2026 trade and analyst coverage.
Source: Aggarwal et al., “GEO: Generative Engine Optimization,” KDD 2024.
Optimising content to surface in answer engines, including AI assistants and pre-LLM answer surfaces such as featured snippets.
Used in SEO industry coverage since around 2019, originally describing optimisation for Bing’s answer features and Google’s featured snippets. Now used interchangeably with GEO in some vendor contexts, though AEO is technically broader because it covers pre-LLM answer surfaces.
A catch-all synonym for GEO and AEO, used primarily in vendor and trade contexts to describe optimisation for large language model–powered surfaces.
Emerged in 2024–2025 as the category proliferated synonyms. Functionally identical to GEO in most usage. Where GEO emphasises the generative nature of the system and AEO emphasises the answer surface, LLMO emphasises the model underneath. The label matters less than the work.
The practice of optimising content to rank in traditional search engine results pages such as Google and Bing, the “ten blue links” a user clicks through to.
The discipline GEO, AEO, and LLMO all evolved from, dating to the mid-1990s. The techniques overlap heavily, covering structured data, authoritative sourcing, and crawlable HTML, but the objective differs: SEO optimises for a ranked link the user clicks, while GEO and AEO optimise for inclusion in a synthesised answer the user may never click through. “GEO vs SEO” is now the defining comparison of the field.
The user-facing term for searches conducted through or mediated by AI assistants such as ChatGPT, Gemini, Perplexity, Claude, and Microsoft Copilot, rather than through a traditional search engine results page.
Used by McKinsey, Bain, and major analysts since 2024 to describe the consumer-side shift. McKinsey projects AI-powered search will mediate around $750 billion in US consumer revenue by 2028. Bain finds roughly 80% of consumers now rely on zero-click or AI-summarised answers for at least 40% of their search needs.
See also: AI Visibility, SEO
Source: McKinsey & Company, “New Front Door to the Internet: Winning in the Age of AI Search,” 2025.
The measurable presence of a brand, product, or piece of content in AI-generated answers, typically measured as mention rate, citation rate, or absorption rate across AI assistants.
Three distinct metrics, in increasing order of strategic value: a mention names the brand without a source link; a citation explicitly attributes information to a URL; absorption measures how much a cited source actually shaped the answer’s language and evidence. Tracking tools that report only citations measure the middle.
Definitions last reviewed: . Maintained by info.link Research.