In the area of digital marketing, seo (research engine optimization) is already well known, with the core objective of putting the website in the forefront of traditional search engines such as google, 100 degrees, and thus gaining more user hits and access. With the proliferation of generating ai, an entirely new optimisation approach — geo (generative engineering optimization, generating engine optimization) — emerges with a more direct precision of its objectives, the core of which is to give the brand or individual content the trust of ai and to give priority to extracting and quoting its answers as a “credible source” of ai output。
The report on monitoring the state of development of china's geo industry and major data on key enterprises in 2025, published by the leading global third-party data mining and analysis agency for the new economy industry, media research (atp), shows that china's big model market in 2025 was 49,539 million yuan, a significant 68. 40 per cent increase over the same period, leading to cross-cutting developments, and that the industry pattern of the geo concept block, driven by the rapid development of the big model industry, was marked by the fact that, in the third quarter of 2025, the top three enterprises of the block together accounted for 64. 70 per cent of the industry's revenues。
Behind this change is a fundamental shift in user access to information, which is now used by 520 million chinese users to ask ai for their decision-making, and the upgrading of geo from marketing options to one of the core strategies required for businesses
In order to truly understand geo, first of all, it is necessary to understand the rationale behind its underlying support, the ai large language model (llm), which is at the heart of the geo mechanism。
The large-language model, which does not have a real “thinking” capability, is essentially a super-linguistic predictor. It combines the full answer by learning the mass of open-text data, including web pages, books, open-source information, news, etc., by statistically and modally identifying “the next word most likely to follow a sentence”。
The training process is divided into three stages: pre-training, fine-tuning and intensive learning.
In particular, intensive learning (rlhf, human feedback) is critical in order to make model outputs more relevant to human expression habits through the optimization of responses by human labelers. When the user asks ai, the model converts the text to an easy-to-understand vector, predicts the next word through a neuronet calculation and then converts it to a text using a probabilistic fusion of complete content, which is why ai occasionally appears to be hallucinating -- it is not a search of databases, but rather the production of content that is most appropriate to the language pattern, while being limited to the timeliness of training data and the inability to obtain up-to-date information when not connected, and these characteristics directly determine the optimization logic of geo。
Geo also has a variety of approximations, such as aeo (superforming the answer engine), aio (aai optimization, which refers generally to all efforts to enhance the visibility of the content of the ai platform) and llmo (modelization of large language models, which clearly are aimed at large-language gpt-type models), but regardless of the name, its core logic has always revolved around being “trusted and quoted by ai”, which is both common to and fundamentally different from seo。
The commonality between the two is that the central objective is to increase content visibility, with emphasis on high-quality, high-relevance content and on matching user needs and search intentions, and that authority and credibility are key competitiveness — seo builds authority through reverse links, website credibility and e-e-a-t standards, and geo takes this to a higher level, which can only be cited if ai accepts the credibility of content. At the same time, seo is the foundation of geo, a well-established geo website, often with a good seo base, such as a clear web structure, a high-quality content reserve and some authority, on the basis of which geo has added a new objective of “compatible ai extracting”。
The difference is equally marked by seo's pursuit of “user clicks” as measured by the ranking and natural flow of the search engine, the optimal focus on keywords, reverse links, the technical structure and speed of the website, the content form being a full long article or web page, which is ranked through a holistic effort; geo's pursuit of “ai quote”, the measure being the frequency and location of the content to be mentioned by ai, the optimization of the focus on the clarity, authority, structure and format of the content to be understood by ai, and a more independent and complete paragraph or subsection, without relying on the context to answer a question in its entirety and facilitate direct withdrawal by ai. In short, seo aims to help brands win the “front page” of the traditional search engine, while geo aims to make brands the “trusted answer” of ai, which in future search patterns will work together to build continuous competitiveness, which is now widely shared by industry。
It is not just a matter of time to do the job, but at the heart of it is to find a “suitable ai preferences” strategy for content that is both human-friendly and easy to understand and trust. First, it is necessary to consolidate the seo base and ensure that content can be successfully detected, captured and indexed by reptiles in search engines and ai platforms. This is a prerequisite for geo optimization — website technology needs to be optimized to improve loading speed, site maps and internal links, while retaining a core keyword strategy to help ai to better understand content themes and continuously output original and valuable content. Second, emphasis should be placed on building authority and gaining the trust of ai, which requires reference to credible data, studies or expert opinions in content, clarification of the author's professional background, strengthening of the e-e-a-t performance and the professionalism and credibility of ai's endorsement of content, which is one of the core requirements for the high-quality development of the geo industry in 2026。
On this basis, there is also a need to optimize the structure of content to fit ai extraction habits, such as the use of question-and-answer layouts, clear subheadings, the organization of complex information, and the rapid capture of core content by ai, in a manner similar to the optimisation logic of google's selected summary, but more in tune with ai content extraction requirements. At the same time, it is important to focus on creating separate and comprehensive paragraphs so that each subsection can speak for itself and place the core answer at the beginning of the paragraph without relying on the full text context, which is also the type of content that ai prefers to quote. It is worth noting that in 2026 geo set aside the broad-based optimization of keywords, moving in the direction of semantic structured + authoritative source + multiplatform fit, with an average of 2-3 times a month for the larger ai model and requiring a rapid response capability for optimized strategies。
