Also called: AI-powered search, AI-native search, generative search, generative AI search, answer engines, AI answer engines
DocumentedGoogle uses the phrase in its own Search announcements; the umbrella scope across engines is common usage, not a defined standard.
AI search is the umbrella name for search experiences in which a language model writes a direct answer, usually with cited links, from sources it retrieves or remembers, instead of or alongside a ranked list of results. It covers answer engines, AI layers inside classic search, and assistants that browse the web on request.
AI search names a family of products rather than one technology. Three shapes dominate. Standalone answer engines, such as Perplexity and ChatGPT search, take a natural-language question and return a synthesized answer with links to sources. AI layers on classic search, such as Google's AI Overviews and AI Mode, sit on top of an existing web index and, in Google's words, are rooted in its core ranking and quality systems. General assistants, such as Claude and Gemini, can fetch web pages during a conversation and answer from what they retrieved.
The boundary is the generated answer: a results page that only ranks links is classic search, even when machine learning does the ranking. The term also stretches to agentic search, where a system plans and runs several searches to finish a task. Google itself titled its May 2026 Search announcement "A new era for AI Search". This glossary uses AI search as its umbrella word because it describes the product without taking a side among GEO, AEO and LLMO, the competing names for the work of being visible in it.
How it works
Most AI search products follow the same loop. The system interprets the question, often rewrites it into several narrower searches (Google calls this query fan-out), retrieves pages or passages from a web index, passes the most relevant ones to a language model, and has the model write an answer that cites some of them. Some answers come from the model's training data alone, without retrieval, which is why the same question can produce a sourced answer in one product and an unsourced one in another. Follow-up questions keep the earlier turns as context.
Why it matters
When buyers ask an AI system instead of scanning ten links, the answer itself becomes the place where a brand is found, compared or left out. A page can rank well in classic results and still be absent from the generated answer, or be cited without earning a click. Teams that care about AI search track a fixed set of buyer questions across several engines, check that each engine's crawlers can reach their pages, and publish content that answers those questions plainly. Engines do not publish how they weigh sources, so results are measured, never promised.
Example
A buyer asks Perplexity for the best CRM for a five-person agency and gets a short comparison that cites three review pages and one vendor blog, without ever opening a results page.
Also called: generative engine, GE, generative search engine, answer engine
DocumentedCoined in the GEO paper (arXiv 2023, KDD 2024).
Generative engine (GE) is the academic name, coined in the 2023 GEO paper, for a search system that uses a large language model to gather information from several sources and write one answer to a query. Google AI Overviews, Perplexity and ChatGPT search are generative engines in this sense.
Aggarwal and colleagues introduced the term in "GEO: Generative Engine Optimization" (arXiv, November 2023; KDD 2024). Their abstract describes "search engines that use generative models to gather and summarize information to answer user queries" and groups them under "the unified framework of generative engines". In the paper's formal model, an engine receives a user's query, retrieves a set of sources, and has a language model write a response that cites those sources inline.
The term is narrower than AI search in one way and broader in another. It describes the system, so it leaves out the interface and the business around it. It also covers any engine that synthesizes from multiple sources, whether or not it is marketed as search. The paper frames the stakes for publishers precisely: users and engines gain, while content creators have "little to no control" over when and how their content is displayed.
How it works
The paper models a generative engine as two steps: retrieval of relevant sources for a query, then generation of an answer grounded in them, with citations attached to sentences. Its experimental engine used GPT-3.5-turbo over the full text of the top five Google results, sampling five answers per query. Production engines add layers the paper does not model, such as query rewriting, passage reranking and safety filtering, but they keep the same retrieve-then-write shape used by retrieval-augmented generation.
Why it matters
The term gives the field a precise object. Asking whether a tactic works on a generative engine forces the follow-up questions: which engine, which retrieval step, which answer format. It also shows why visibility is two problems. A page must first be retrieved into the engine's source set, and then be used and cited in the written answer. Teams that separate the two can tell whether a missing citation is a crawling and retrieval issue, fixed with access and indexing work, or a content issue, fixed by making the page more useful to quote.
Example
When Google AI Mode answers a question about fixing a weedy lawn by running several related searches and writing one response with links, it is acting as a generative engine in the paper's sense.
Also called: GEO, Generative Engine Optimization, generative engine optimisation, generative search optimization, AI search optimization
DocumentedEmergingThe coinage and academic definition are documented in the paper; industry usage is broader and still shifting.
GEO (Generative Engine Optimization) is the practice of improving how often and how prominently content is used and cited in answers written by generative engines such as Google AI Overviews, ChatGPT search and Perplexity. The name comes from a 2023 research paper and is one of the most widely used labels for AI search work.
Aggarwal et al. coined GEO in November 2023 as "the first novel paradigm to aid content creators in improving their content visibility in generative engine responses", framed as black-box optimization: the creator cannot see inside the engine and can only change the content and observe the result. The paper measured visibility with two metrics, the share of an answer attributed to a source weighted by citation position, and a model-judged impression score.
Industry usage since 2024 is much wider. Agencies and tool vendors use GEO for crawler access, structured data, entity and brand work, digital PR, third-party mentions and answer monitoring, most of which the paper never tested. Andreessen Horowitz's May 2025 essay carried the term into marketing by recasting the goal around "reference rates". Google acknowledged the vocabulary in its May 2026 guide, noting that terms like AEO and GEO are common online while calling optimization for its generative features "still SEO". In practice GEO, AEO and LLMO often describe the same work; this glossary treats them as names for one field it calls AI search.
How it works
The paper tested nine rewriting methods on its GEO-bench queries. Adding citations to sources, adding quotations and adding statistics lifted visibility the most, by roughly 30 to 40 percent on the position-weighted metric, while keyword stuffing did not help. Effects varied by domain, and the largest gains went to sources the retrieval step had ranked low. Industry practice adds levers outside the text: allowing AI crawlers, keeping pages indexable and snippet-eligible, earning mentions on sites engines often cite, and tracking answers over time across engines.
Why it matters
GEO is the word most buyers, vendors and job titles use, so it shapes budgets and briefs. Knowing its two meanings prevents two common mistakes: treating a 2023 benchmark result as a manual for today's engines, and treating a vendor's tactic list as research. A 2026 critical survey of 45 GEO studies found that content changes can alter how already-retrieved content is cited, but that no reviewed technique showed a stable cross-platform effect on being discovered in the first place. Teams should test changes on their own buyer questions and expect variance.
Example
A software company rewrites its pricing page so the plan comparison sits in one self-contained paragraph with current figures and a cited source, then tracks for eight weeks whether ChatGPT search and Perplexity start citing that page for pricing questions.
DocumentedEmergingGoogle names the term in its May 2026 guide; the 2017 origin is Kalicube's own account; the LLM-era scope is still shifting.
AEO (Answer Engine Optimization) is the practice of shaping content so that answer engines, voice assistants and AI systems can lift it as a direct answer to a question. The term predates large language models, first describing work on featured snippets and voice results, and now often serves as a synonym for GEO.
AEO aims at the answer rather than the ranked link: the unit of success is a passage that a system can quote or read aloud as the reply. SEO consultant Jason Barnard of Kalicube says he named the practice publicly at BrightonSEO in 2017 and presented it at a Trustpilot event in January 2018; that dating comes from Kalicube's own pages, which do not all agree on the year. In that first era the targets were featured snippets and voice assistants.
From 2023 the same label was reapplied to LLM answer engines, and usage now splits two ways. Some writers treat AEO as a narrower subset of GEO, focused on winning the direct answer; others treat it as the older and more accurate umbrella, since "answer engine" describes the product well. Google's May 2026 guide spells out both AEO and GEO, notes they are common online, and says that optimizing for its generative features is still SEO. Neither camp holds authority, and both usages are live.
How it works
AEO craft works at the level of the question and the passage. A team lists the questions buyers ask, places each one as a heading or near the top of a section, answers it in a short self-contained paragraph, and supports the answer with specifics a system can verify. In the snippet era the reward was a position-zero box in one search engine; with LLM engines the reward is being retrieved as a passage and quoted or cited in the generated answer.
Why it matters
When a stakeholder says AEO, it helps to ask which era they mean. Snippet-era AEO is measured by featured-snippet wins and voice answers in one search engine. LLM-era AEO is measured by mentions and citations across several AI engines, sampled repeatedly because answers vary from run to run. The writing tactics overlap heavily, so most teams can serve both with the same content work. The measurement does not overlap, so every report should name the surfaces it covers.
Example
Under the heading "What is query fan-out?", a documentation page opens with a 50-word definition that an answer engine can quote whole, followed by the detailed explanation.
Kalicube, Jason Barnard: The Architect Behind Kalicube Pro (self-reported origin of the term), published n/d, read 2026-11-07: https://kalicube.pro/about/jasonbarnard
Also called: AI Overviews, AIOs, AI Optimization, AIOSEO, All in One SEO
ObservedEach meaning appears in a named source; no authority governs the acronym.
AIO is an acronym with three live meanings in search marketing: Google's AI Overviews, the common use in SEO tools and data studies; AI Optimization, an occasional name for the whole discipline; and AIOSEO, the All in One SEO plugin for WordPress. Reading a report correctly depends on knowing which one is meant.
The acronym collides because three groups adopted it independently. SEO tools and data studies abbreviate Google's AI Overviews as AIO, with "AIO keywords" and "AIOs" as common forms; Ahrefs' October 2024 analysis of 300,000 keywords uses both. A smaller group of agencies and software firms uses AIO for AI Optimization, the work of making content visible and cited in generative AI systems; the German company axite lists AIO alongside GAIO, GEO, LLMO and AEO as names for that work. Finally, AIOSEO is the brand of the All in One SEO plugin, which its maker says runs on more than 3 million WordPress sites, so the letters also turn up in plugin settings and documentation.
The first meaning is one Google surface, the second is the whole field, and the third is a product. Only the context tells them apart.
Why it matters
A metric named with AIO usually measures Google only. An "AIO citation rate" or "AIO visibility" figure in a vendor report describes presence inside Google AI Overviews, and says nothing about ChatGPT, Perplexity, Gemini or even Google AI Mode, which is a separate surface. Teams comparing reports should confirm the surface behind every acronym, and write "AI Overviews" in full in their own briefs and dashboards so that a channel-level number is never read as a field-level one.
Example
A monthly report states "AIO visibility up 20 percent". The underlying data covers only US keywords that triggered Google AI Overviews, so it cannot support a claim about AI search overall.
Also called: LLMO, LLM optimization, LLM optimisation, LLM SEO, large language model optimization
EmergingPractitioner coinage with no canonical definition and no documented first use.
LLMO (Large Language Model Optimization) is the practice of shaping a brand's presence so large language models describe it accurately and mention or cite it, whether they answer from training data or from live web retrieval. A practitioner coinage with no canonical definition, it is one of the field's three main names with GEO and AEO.
LLMO puts the model, rather than the search product around it, at the center. Its distinctive concern is what a model absorbed during training: how it recalls a company's category, founding facts, products and reputation when it answers without searching. It also covers the retrieval side it shares with GEO, since most assistants now search the web for many questions.
No paper or standards body defines the term, and no documented first use was found. Writers often use LLMO, GEO and AEO interchangeably, and some note that the labels do not map reliably onto the training-versus-retrieval distinction they seem to imply. LLMO is the broadest of the three names and the hardest to act on directly, because outside the model labs nobody controls which documents enter a training set or how heavily they count.
How it works
Two channels feed a model's picture of a brand. Training data is fixed at the model's knowledge cutoff and reflects whatever the open web, licensed sources and reference sites said before then. Retrieval adds fresh pages at answer time when the assistant decides to search. LLMO work on the first channel is indirect: keeping facts consistent across a company's own site, reference databases such as Wikidata, and independent coverage, so that whatever gets collected later says the same thing. Work on the second channel is the same as GEO.
Why it matters
Many assistant answers, especially quick ones, come partly or wholly from what the model remembers, with no citation to check. If that memory is outdated or wrong, a buyer gets an inaccurate description of a product without visiting any page that could correct it. Teams address this by auditing what major models say about them with web search turned off, correcting the public record where errors start, and repeating the audit after new model releases. Effects are slow and hard to attribute, so claims of fast LLMO results deserve skepticism.
Example
A company finds that two assistants, answering without web search, give a founding year three years off. It corrects the date on its About page, its Wikidata item and its press kit, then rechecks after the next model updates.
Also called: AI search engine optimization, AI search optimization, SEO for AI, AI-powered SEO, AI SEO automation
ObservedEmergingBoth meanings appear in vendor guides; there is no standard definition.
AI SEO is an ambiguous label with two common meanings: optimizing content and sites so AI search engines retrieve and cite them, a synonym for GEO; and using AI tools to do SEO work such as research, drafting and audits. Vendors use both, sometimes on the same page, so contracts should state which.
The tool meaning is the older of the two and is common in SEO software marketing. Semrush's May 2026 guide defines AI SEO as the practice of using AI tools to improve and streamline the SEO workflow, and Ahrefs' May 2024 guide to using AI for SEO covers keyword research, drafting, optimization, technical tasks and reporting. The same Semrush guide then lists getting cited in AI search platforms as one of its uses, which shows how easily the two meanings blend.
The optimization meaning treats AI SEO as plain-language shorthand for GEO or AEO: making pages discoverable, extractable and citable by AI Overviews, AI Mode, ChatGPT search and similar engines. Google's May 2026 guide supports the underlying idea from its side, saying that optimizing for its generative AI features is still SEO, without using the phrase "AI SEO" itself. Agentic SEO, where AI agents carry out multi-step SEO work, is a newer branch of the tool meaning.
Why it matters
The two meanings lead to different engagements, budgets and success metrics. An AI SEO project in the tool sense is judged on the speed and cost of SEO production and on classic rankings; in the optimization sense it is judged on mentions and citations in AI answers. Misreading the label can mean buying content automation when the goal was AI visibility, or the reverse. Teams should define the term in every brief and contract, and check that any automated drafting still produces pages worth citing.
Example
One agency proposal titled "AI SEO" offers automated keyword clustering and article drafts; another with the same title offers prompt tracking across ChatGPT and Perplexity. They are different services sold under one name.
Semrush (Cecilia Meis), 11 ways to use AI for SEO (+best practices & challenges), published 2026-05-11, read 2026-11-07: https://www.semrush.com/blog/ai-seo/
Ahrefs (Ryan Law), 14 Ways to Use AI for Better, Faster SEO, published 2024-05-16, read 2026-11-07: https://ahrefs.com/blog/ai-seo/
Promptwatch, AI Search & GEO Glossary (AI Search Engine Optimization and AI SEO Automation entries), published n/d, read 2026-11-07: https://promptwatch.com/glossary
Also called: GAIO, Generative AI Optimization, Generative AI Optimisation, AI Optimization (AIO)
EmergingA German-market agency term; the attribution to Philipp Kloeckner rests on one agency article.
GAIO (Generative AI Optimization) is a name for the work of making content visible, cited and recommended in generative AI answers, used mainly by German-speaking agencies and publishers. It covers the same ground as GEO and has no canonical definition. Some glossaries list it as an alias of AI Optimization (AIO).
The agency Hotwire Global credited German SEO expert Philipp Kloeckner with proposing the name in a March 2024 article. That article called the discipline "largely still hypothetical", aimed it at brand visibility in chatbots such as ChatGPT, Bing Chat and Google Bard, and argued that brand mentions in AI answers matter more than backlinks. Since then the German hosting company IONOS has published a GAIO explainer in its Digital Guide (July 2026), and the Stuttgart software firm axite treats GAIO and AIO as synonyms.
The definitions in these sources match GEO's industry meaning: content, structure and technical foundations adapted so that ChatGPT, Gemini, Copilot and similar systems select and reuse a brand's information. None of them describes a method distinct from GEO, so the difference is mostly regional preference and branding. IONOS adds a useful caveat that the work does not guarantee citation.
Why it matters
For teams working in German-speaking markets, GAIO is a term clients and agencies may use in briefs, tenders and job titles, so recognizing it avoids paying twice for the same scope under two names. For everyone else it is a reminder that the field's vocabulary is fragmented by region and by vendor. When a proposal uses GAIO, compare its deliverables with a GEO or AI search scope line by line: crawler access, content structure, mentions on frequently cited sources, and answer monitoring.
Example
A Munich retailer's tender asks for GAIO services; the scope listed (AI crawler access, structured product data, answer monitoring in ChatGPT and Gemini) matches a GEO engagement item for item.
ObservedSEO vocabulary whose origin is traced by a secondary source; rare and unstandardized as an AI-search term.
SXO (Search Experience Optimization) is the practice of optimizing the whole experience of a searcher, from the result they see to the page they land on and the action they take, by combining SEO with user experience and conversion work. It predates AI search and is occasionally stretched to cover AI answers.
SXO shifts the focus of search work from pleasing the engine to serving the person searching: titles and snippets that set accurate expectations, pages that answer the query fast, and paths to conversion that keep the visitor. The marketing publisher OMR traces the idea to 2008, when marketer Bryan Eisenberg proposed recasting SEO as "searcher experience optimization" at the SES conference in San Jose. That origin is OMR's account; the original talk could not be checked.
In AI search writing, SXO occasionally reappears as optimizing the searcher's experience across classic results and AI answers. That usage is rare and has no agreed definition. The term's main value today is historical: it shows the field renaming the same move, from ranking toward experience, every few years.
Why it matters
For AI search, SXO works as a caution and a reminder. The caution: a new acronym does not mean new work, so proposals should be judged on deliverables. The reminder: when an AI answer does send a click, the visitor arrives with a specific question already partly answered, so the landing page must confirm the answer quickly and offer a clear next step. Pew's 2025 study found that users clicked a link inside Google's AI summaries on just 1 percent of visits, which makes each of those clicks worth converting.
Example
A visitor clicks a cited link in an AI answer about invoice software for freelancers and lands on a generic homepage. A page that opens with the freelancer plan, its price and a signup button would match the question that brought them.
Also called: AX, Agent Experience, Agent Experience Optimization, agent readiness, agentic web optimization, agent-friendly design
DocumentedEmergingThe coinage is documented in Biilmann's January 2025 essay; no independent measurement of its effect exists.
AX (Agent Experience) is the experience AI agents have when they use a product, website or API on a person's behalf, and the work of making that experience good: easy access, clear APIs, machine-readable documentation and flows agents can complete. Mathias Biilmann of Netlify coined the term in January 2025.
Biilmann's essay "Introducing AX: Why Agent Experience Matters" (2025-01-28) describes AX as "the holistic experience AI agents will have as the user of a product or platform", and places it after user experience (UX) and developer experience (DX). The elements he lists are access (how an agent gets permission to act for a user), APIs that agents can operate, documentation and context written for language models, tools and SDKs, and flows where people and agents work together.
AX differs from GEO in its target. GEO aims at answers that people read; AX aims at agents that navigate, fill forms, call APIs and transact. The two meet whenever an agent searches before it acts. Other writers use neighboring labels for overlapping work, including "agent experience optimization" in glossaries and "agentic web optimization" in a June 2026 Semrush guide that stacks SEO foundations, agent readiness, off-site presence, an action layer and protocols such as MCP.
Why it matters
As assistants gain the ability to book, buy and configure on a user's behalf, a product an agent cannot operate risks being passed over for one it can. Google's May 2026 guide already addresses browser agents that read pages through screenshots, the DOM and the accessibility tree. Teams start with basics that also help people: semantic HTML, documented APIs, stable form labels, machine-readable docs, and sign-in flows that support delegated access. Evidence on which AX changes win an agent's choice is still thin and mostly anecdotal.
Example
A travel site exposes its availability search through a documented API and labels every booking form field clearly, so a browsing agent asked to find a two-night stay can complete the reservation without a human taking over.
Also called: Agentic Search Optimization, agentic SEO workflows, AI agent SEO, agent search optimization
EmergingClaimedVendor usage in two senses, with no standard definition or independent measurement.
Agentic SEO is a label with two uses: running SEO and GEO work through AI agents that plan, act and adapt across multi-step workflows under human review; and, less often, optimizing a site for AI agents that search, compare and act for users, which overlaps with AX. Ahrefs and Promptwatch both use the first meaning.
In the first sense, an agent is an AI system that can call tools, read data and take actions, so agentic SEO hands it tasks such as auditing pages, clustering keywords, drafting briefs, monitoring rankings and AI citations, and fixing simple issues. Ahrefs' May 2026 guide frames it as agents that take actions inside SEO workflows and recover from problems, as opposed to chatbots that only generate text, and stresses that agents still need a human in the loop for anything that matters, especially client-facing work.
In the second sense, the target is the agent as a visitor: making content and actions usable by assistants that browse, compare and buy for people. That work is better named AX, or agentic web optimization as Semrush calls it, and this glossary files it under AX. Both senses are vendor usage without a standard definition.
Why it matters
Agentic tooling changes the cost of AI search work, since monitoring dozens of buyer questions across several engines, or auditing thousands of pages for crawlability, becomes cheap to repeat. It also adds risk: agents that publish or edit without review can spread errors at scale, and Ahrefs warns that agents are more error-prone in long, hands-off runs. Teams adopting it keep a human approval step for anything published, log what each agent changed, and judge tools by the quality of their output rather than by the word "agentic" in the pitch.
Example
An agent runs weekly: it queries ChatGPT search and Perplexity with 40 tracked buyer questions, records which pages are cited, flags pages that lost citations, and drafts suggested fixes that an editor approves before anything goes live.
Ahrefs (Mateusz Makosiewicz), What Is Agentic SEO? And How to Get Started This Week, published 2026-05-19, read 2026-11-07: https://ahrefs.com/blog/agentic-seo/
Also called: AI search visibility, LLM visibility, AI brand visibility, visibility in AI answers
EmergingA discipline name in growing use; its underlying metrics are defined by vendors and vary by tool.
AI visibility is the state the whole field aims at: whether, where and how a brand, site or fact appears in AI-generated answers across engines, measured mainly as mention rates and citation rates on a fixed set of prompts. Teams often prefer it as a neutral name that sidesteps the GEO, AEO and LLMO dispute.
AI visibility names an outcome rather than a method, which is why it travels well between teams that disagree on the acronyms. It has three layers: presence (is the brand mentioned or its page cited at all), prominence (how early and how much of the answer it occupies) and portrayal (whether the description is accurate and favorable). Tool vendors have adopted the phrase as a product category; Semrush's AI Visibility Toolkit, for example, reports a 0 to 100 visibility score alongside mentions, cited pages, share of voice and sentiment across models.
The term follows the shift Andreessen Horowitz described in 2025, when it argued that "reference rates" now matter alongside click-through rates. The publisher Offpage Atlas recommends the phrase precisely because GEO, AEO and LLMO have no official definitions, and suggests reporting brand mentions and page citations as two separate rates. The individual metrics are defined in this glossary's measurement section.
How it works
Measuring AI visibility means sampling. A team fixes a panel of prompts that real buyers would ask, runs each one several times per engine because answers vary from run to run, and records for every answer whether the brand is mentioned, which URLs are cited, in what order, and how the brand is described. Aggregated, these runs give a mention rate, a citation rate, share against competitors and sentiment. Results hold only for the prompts, engines and dates sampled, and should be reported with those limits.
Why it matters
AI visibility is the number leadership asks about once buyers start researching in assistants: do we appear when people ask about our category, and what does the answer say? Without a defined panel and repeated runs, the answer is anecdote, since one screenshot of one answer proves little. Teams set a baseline across the engines their buyers use, track it monthly, and tie changes to specific actions such as new pages, fixed crawler access or earned coverage, while accepting that engines do not explain their source choices.
Example
A cybersecurity vendor runs 60 buyer questions five times each in ChatGPT, Perplexity, Gemini and Google AI Mode every month, and reports that it was mentioned in 34 percent of answers and cited in 12 percent, up from 28 and 9 percent the quarter before.
Semrush Knowledge Base, Getting Started with the Semrush AI Visibility Toolkit: A Step-by-Step Guide, published n/d, read 2026-11-07: https://www.semrush.com/kb/1496
Andreessen Horowitz (Zach Cohen, Seema Amble), How Generative Engine Optimization (GEO) Rewrites the Rules of Search, published 2025-05-28, read 2026-11-07: https://a16z.com/geo-over-seo/
ObservedClaimedThe multi-platform search behavior behind the label is observed; the label as a named discipline is agency positioning.
Search Everywhere Optimization is a marketing label for optimizing a brand's organic presence on every platform where people look for answers, including Google, YouTube, TikTok, Reddit, Amazon and AI assistants such as ChatGPT, instead of on Google alone. Neil Patel and his agency NP Digital promote it, notably in a January 2025 article.
The idea is that search behavior has spread across many platforms, each with its own discovery system: social video, marketplaces, forums, app stores, maps, voice assistants and AI chat. Patel's article "Search Everywhere Optimization: The New SEO" (2025-01-06, updated 2025-01-20) names Google, Bing, YouTube, Instagram, Facebook, TikTok, Pinterest, Reddit, Quora, podcasts, voice assistants, app stores, maps and AI tools among the places to optimize, and says his team is pioneering the approach at NP Digital. It claims credit for the approach and does not say who coined the name.
As a named discipline, the label is agency positioning. The behavior it points to is real and visible: AI answers in particular draw on platforms such as Reddit and YouTube, which Pew found among the three most-cited sources in Google's AI summaries in March 2025. AI search is one surface inside this wider frame.
Why it matters
AI engines cite and summarize content from far beyond a brand's own site: forum threads, videos, review platforms and marketplace listings. A brand absent from those places gives an answer engine less to find and describe, even with a strong website. The useful part of the label is the inventory it forces: list where buyers in a category ask questions, check which of those sources AI answers cite, and build presence there with accurate, helpful contributions. The label itself adds no method beyond that.
Example
A cookware brand finds that AI answers about the best nonstick pan cite two Reddit threads and a YouTube review far more often than any brand site, so it sends review samples to the channel and answers questions in the subreddit from a disclosed company account.
Also called: conversational information seeking, conversational information retrieval, multi-turn search, chat search, dialogue search
DocumentedObservedA research concept documented since at least 2017; usage figures for AI interfaces come from vendor clickstream panels.
Conversational search is a way of searching in natural-language turns, where the system keeps the context of earlier questions, can ask or suggest follow-ups, and answers in prose rather than returning a fresh list of links each time. It is the default interaction mode of AI assistants and of Google's AI Mode.
Information retrieval research studied conversational search well before chat assistants went mainstream. Filip Radlinski and Nick Craswell's "A Theoretical Framework for Conversational Search" (CHIIR 2017) proposed a small set of properties that together measure how conversational a system is, and modeled the actions a conversational search agent can take. In practice the defining traits are memory across turns, a system that can take initiative (for example by asking a clarifying question), and answers composed for the conversation.
AI search products made the mode common. Google introduced AI Mode in March 2025 for complex, multi-part questions with follow-ups, and since May 2026 lets users continue from an AI Overview into an AI Mode conversation with the context carried over. The boundary with classic search is memory: a series of separate keyword queries is not conversational, even when each one returns an AI answer.
How it works
In a conversational engine, each new turn is interpreted together with the earlier ones, so "and for a team of five?" inherits the product category from the previous question. The engine may rewrite the turn into one or more standalone searches before retrieving sources, then answer within the thread. Per Semrush's clickstream panel, ChatGPT prompts without web search averaged 23 words in the second half of 2024, while search-enabled queries averaged 4.2 words; Semrush's 2026 update reports non-search prompts falling from 24.9 to 13.5 words and search-enabled prompts rising from 4.7 to 8.7 words between early 2025 and early 2026. The widely repeated "23 words versus 4" comparison, popularized by Andreessen Horowitz, sets those two ChatGPT figures side by side; neither one measures Google.
Why it matters
Conversational prompts are longer and more specific than keyword searches, and follow-ups narrow the need further, so the questions an engine searches for on a buyer's behalf rarely match a classic keyword list. Content that answers specific situations, comparisons and constraints gives an engine something to retrieve at each turn. Teams should build prompt panels from real buyer conversations, sales calls and support tickets as well as keyword tools, and test multi-turn sequences, since appearing in the answer to the first question does not guarantee presence after the follow-up.
Example
A buyer asks an assistant for project management tools for agencies, then adds "only ones with client portals" and "under 20 dollars a seat". The final answer cites different sources from the first one, because each turn changed what the engine searched for.
Also called: zero-click searches, zero-click rate, no-click search, zero-click SERP
MeasuredObservedRates come from clickstream panel studies (SparkToro on Similarweb data, 2026; Pew Research, 2025), each one study with stated limits; how much of the rise AI answers cause is not isolated.
Zero-click search is a search that ends without the user clicking through to any website, either because the results page, an AI answer or a feature such as a snippet or map already gave the answer, or because the user gave up. In AI search it names visibility that earns exposure without a visit.
A zero-click search is counted at the level of a search session: the user types or speaks a query, sees a results page, and then leaves, refines the query or stays on the engine's own properties without visiting an outside site. The term predates generative AI. It grew out of clickstream studies of Google results pages filled with featured snippets, knowledge panels, maps and instant answers, and it now also covers AI Overviews, AI Mode and assistants that answer in full.
The boundary matters. A zero-click search can still deliver value to a brand (a name read in an answer, a phone number called from a map), and studies often count clicks to Google-owned properties such as YouTube or Maps separately from clicks to the open web. Rates depend on the panel, the device mix, the country and whether app traffic is counted, so figures from different studies rarely compare directly.
How it works
Researchers estimate the rate from clickstream panels: consenting users whose browsing is logged, so a search can be followed to a click or to nothing. SparkToro's 2026 analysis used Similarweb's desktop and mobile web panel for US Google searches from January to April 2026 and found that 68.01% ended without a click, against 60.45% in its 2024 study built on Datos data. The author warns that the two panels are not directly comparable and that searches made in the Google app were excluded. Pew Research's March 2025 panel of 900 US adults found users clicked a traditional result on 8% of visits when an AI summary appeared, versus 15% without one.
Why it matters
When most searches end on the results page, being named or cited inside the answer becomes the main outcome a brand can win, and traffic stops being a complete measure of search visibility. Teams respond by tracking presence in answers (citation share, brand mentions) alongside clicks, by making the facts they want repeated easy to quote, and by offering reasons to visit that a summary cannot replace, such as tools, original data or configurators. Evidence on how much AI answers drive the trend is thin: AI Mode accounted for only 0.34% of searches in SparkToro's data, and the change of panel between 2024 and 2026 complicates pinning the rise on any single feature.
Example
A homeowner searches 'how long does a heat pump last', reads the AI Overview giving a typical lifespan range with two cited sources, and closes the tab. The manufacturer cited in the overview earned an impression in Search Console's generative AI report but no session in its analytics.
Also called: agent-led commerce, agentic shopping, AI agent commerce, agentic checkout, conversational commerce (partial overlap)
DocumentedEmergingPrograms and protocols are documented by their operators (Google, Microsoft, OpenAI, Stripe); the term's scope is still settling and adoption figures are mostly vendor-reported.
Agentic commerce is shopping in which an AI assistant or agent does part of the buying work for a person: finding products, comparing offers, filling a cart and, in some systems, paying at checkout. It turns AI answers into a sales channel where product data and checkout integrations decide which merchants get recommended and chosen.
Agentic commerce is the commercial end of agentic search. Google describes it as AI completing tasks on people's behalf, and its January 2026 retail announcements said checkout would come to eligible product listings in AI Mode and the Gemini app. Microsoft launched Copilot Checkout in the US on 2026-01-08 with PayPal, Shopify and Stripe, with merchants remaining merchant of record. OpenAI launched Instant Checkout in ChatGPT on 2025-09-29, then said in 2026 that it was moving away from a standalone in-chat checkout; purchases now complete on merchants' own sites or apps while ChatGPT concentrates on product discovery.
The term spans three layers that are easy to confuse: discovery (an assistant recommends products from feeds, catalogs and web content), delegated action (an agent operates a browser or calls an API to fill a cart), and payment (scoped tokens or credentials let the agent pay with the user's consent). Open protocols such as the Agentic Commerce Protocol, the Universal Commerce Protocol and the Agent Payments Protocol standardize parts of these layers. A shopping assistant that only answers questions sits at the edge of the term.
How it works
A merchant exposes structured product data (a feed or catalog), a machine-readable description of its commerce capabilities and a checkout interface an agent can call. The assistant matches products to the request and the user's context, presents options and, where checkout is supported, hands payment to a provider that issues a scoped token, so the merchant receives an order without the shopper visiting its site. Google's Universal Commerce Protocol has businesses publish their capabilities in a JSON manifest at /.well-known/ucp; OpenAI's commerce documentation centers on product feeds plus checkout and delegated payment specifications.
Why it matters
The recommendation and the sale can now happen inside an assistant, so the question for a merchant shifts from ranking a product page to being represented accurately in the data an agent reads: price, availability, shipping, returns and reviews. Teams act by keeping product feeds complete and fresh, checking eligibility for each assistant's merchant program, deciding whether to support agent checkout or send buyers to their own site, and measuring orders that start in an assistant. Adoption is early and uneven: OpenAI's move away from in-chat checkout, reported in March 2026, shows the model is still being worked out.
Example
A user asks AI Mode for a carry-on suitcase under $200 that fits a specific airline's size limits. The answer lists three products, and for the retailer integrated through UCP the user pays with Google Pay without leaving the conversation, while the retailer stays the seller of record.
EmergingUsed since late 2025 by investors, vendors and newsletters in two different senses; no source establishes who used it first.
Agent-Led Growth (ALG) is a go-to-market idea named after product-led growth, used in two senses: growth that comes when AI agents discover, evaluate, recommend or buy products for people, and growth driven by companies that deploy their own agents to sell. The first sense is the one that concerns AI search.
The term is recent and unsettled. Insight Partners wrote in March 2026 that it was already in use for two different things under one label. On the demand side, agents work for the buyer: an assistant researches a category and recommends a vendor, a coding agent picks the tool whose documentation it can read, a personal agent compares and purchases. On the supply side, a company deploys agents to reach buyers more efficiently, for example agents that prospect, write outreach and follow up.
Glossaries and vendors pick one side or both. Lusha's glossary (2025) and Expertise AI (February 2026) use the supply-side sense; Lightsage, a startup that raised a $4 million seed round in September 2026, uses the demand-side sense for software sold to agents; Insight Partners and the newsletter Agent-led Growth (August 2026) describe both. Like product-led and sales-led growth, it names a motion, and a company can run several at once.
Why it matters
In the demand-side sense, the first reader of a company's website, documentation, pricing and reviews is increasingly a machine acting for a buyer. Being chosen by that reader depends on what AI search measures: whether answers cite the company, whether agents can read and act on its pages, and whether its facts are consistent enough to trust.
Example
A developer asks a coding agent to add error monitoring to a project; the agent picks the tool whose install steps and pricing it can read and verify, and the vendor gains a customer without a sales call.
SiliconANGLE, Lightsage gets $4M in funding to help software makers sell directly to autonomous AI agents, published 2026-09-08, read 2026-11-08: https://siliconangle.com/?p=845974