Introduction
As AI tools such as ChatGPT, DeepSeek, Doubao, Yuanbao, and Kimi increasingly become important information gateways for users, the logic of brand exposure is changing.
In the past, users mainly entered keywords into search engines, browsed web pages, and clicked links. In AI search scenarios, users are increasingly asking AI questions directly and expecting AI to provide comprehensive answers. Relevant industry research shows that generative AI Q&A entry traffic in China has already reached a relatively high proportion and has begun to surpass traditional search in some scenarios. According to a survey released by NPDigital in April 2026, AI tools accounted for 36% of answer-seeking behavior among global users.
Against this backdrop, GEO, or Generative Engine Optimization, has begun to enter the corporate agenda. It is not simply an extension of traditional search marketing concepts, but a new direction for building brand visibility in the era of AI search.
To understand GEO, it is first necessary to understand the industry's development trajectory. Only by understanding where the field came from and what stage it is currently in can businesses make a more rational assessment of its capabilities and more clearly evaluate the real experience of relevant service providers.
I. GEO Addresses a New Problem in the AI Search Era
GEO stands for Generative Engine Optimization. Its core objective is to enable brand information to be understood, retrieved, trusted, and appropriately cited by generative AI when AI answers users' questions.
In other words, SEO mainly addresses the question: "After users search, can they see my webpage link?"
GEO mainly addresses the question: "When users ask AI, will AI accurately understand me, cite me, and recommend me?"
Compared with SEO, GEO faces different scenarios, content formats, and evaluation logic. The key differences between the two are mainly reflected in the following areas:
1. Different Optimization Objectives
SEO focuses more on brand rankings, exposure, and click opportunities on search results pages. GEO focuses more on whether brand information can enter AI answers and whether it is accurately expressed and positively presented.
Simply put, SEO aims to "be seen and clicked by users"; GEO aims to "be understood and cited by AI."
2. Different Traffic Paths
The typical SEO path is:
Search keywords → Browse search results → Click a link → Enter the website
In AI search scenarios, the path is becoming:
Ask a question in natural language → AI integrates multiple sources to generate an answer → Users obtain information directly → Brand awareness, trust, or subsequent action is formed
This means users may no longer click on a particular website, but instead receive information directly from an AI answer. The competition for brand visibility is therefore shifting from "position in a list of links" to "the way the brand exists within an AI answer."
3. Different Content Approaches
SEO content typically revolves around keywords, page topics, ranking opportunities, and user click intent.
GEO content places greater emphasis on:
- Answer-first content;
- Clear structure;
- Verifiable facts;
- Information that can be extracted by AI;
- Content suitable for direct citation in AI answers.
For example, in GEO scenarios, content cannot merely provide a general introduction to a brand. It also needs to answer specific questions that users may ask AI, such as:
- What scenarios is this brand suitable for?
- How is it different from similar products?
- What are the price, delivery cycle, and applicable industries?
- What real cases or data can prove its value?
- What should users pay attention to when using it?
When these points are expressed clearly, structurally, and verifiably enough, they are more likely to be cited by AI.
4. Different Ways of Establishing Authority
SEO places greater emphasis on external links, page authority, and search ranking signals.
GEO places greater emphasis on whether a brand has established a trustworthy, consistent, and verifiable information network across the web.
In AI search scenarios, AI does not only examine what a single page says. It also combines multiple information sources to determine whether brand information is reliable. Therefore, GEO places greater emphasis on:
- Consistent brand information across multiple platforms;
- No contradictions among statements on the official website, media outlets, industry platforms, and community discussions;
- Real cases, data, credentials, and expert endorsements;
- Content that meets E-E-A-T requirements, namely experience, expertise, authoritativeness, and trustworthiness;
- Brand information that can be cross-verified by AI.
In other words, GEO is not simply about making an individual page easier to see. It is about making the brand as a whole easier for AI to trust.
5. Different Optimization Units
SEO typically uses individual webpages, individual keywords, or individual page topics as optimization units.
GEO places greater emphasis on brand entities, topic clusters, and knowledge networks.
In GEO, businesses need to enable AI to understand:
- Who the brand is;
- What products or services it provides;
- What problems it solves;
- Which customers it serves;
- How it differs from competitors;
- What cases, data, and credentials support it;
- What position the brand occupies within the industry.
Therefore, GEO is not single-page optimization. It is about building a complete knowledge system around the brand that can be understood and cited by AI.
6. Different Measurement Methods
SEO mainly focuses on:
- Keyword rankings;
- Organic search traffic;
- Page click-through rate;
- Website traffic;
- Page conversion performance.
GEO focuses more on:
- Brand mention rate in AI answers;
- Accuracy of cited brand information;
- Brand position within AI answers;
- Sentiment toward the brand in AI descriptions;
- Changes in branded search volume;
- Changes in direct traffic;
- Changes in user inquiries, leads, or sales cycles.
Therefore, GEO performance cannot simply be evaluated using SEO ranking and click metrics. A new evaluation system focused on visibility in AI answers needs to be established.
7. More Fragmented Platform Rules
Although SEO also changes as search engine algorithms evolve, its overall optimization targets are relatively concentrated.
GEO, by contrast, faces multiple AI platforms, and different platforms have different source preferences, content comprehension methods, and citation logic.
For example:
- DeepSeek is relatively sensitive to authoritative sources, technical documentation, structured data, and FAQ content;
- ChatGPT places greater emphasis on official brand websites, E-E-A-T signals, and authoritative content;
- Google Gemini and AI Overviews are more sensitive to Schema, knowledge graphs, official brand websites, and content within the Google ecosystem;
- Perplexity relies more heavily on community discussions, video content, and third-party sources;
- Domestic platforms such as Doubao, Kimi, Wenxin Yiyan, and Tencent Yuanbao also have their own content ecosystems and source preferences.
Therefore, GEO cannot use one fixed template for all platforms. It requires differentiated adaptation for different AI platforms.
II. From the Timeline, GEO Is a New Field That Has Gradually Taken Shape in Recent Years
When GEO is placed within the history of the industry, a relatively clear timeline can be seen.
1. Academic Concept Stage: Around 2024
As a clearly defined concept, GEO can be traced back to relevant academic research published at KDD 2024. Research institutions represented by Princeton University began systematically studying the question of "how to make content more visible in AI-generated answers."
This means that GEO was initially defined through academic research. At the time, generative AI search was rapidly emerging. AI was no longer merely a chat tool, but was beginning to take on the roles of information retrieval, answer integration, and decision support.
2. Industry Emergence Stage: 2024–2025
As the user base of generative AI products such as ChatGPT rapidly expanded and interest in AI search engines such as Perplexity increased rapidly, GEO began moving from an academic concept into industry discussions. Relevant sources indicate that interest in the AI search engine Perplexity once surged by 858% year over year.
During this period, businesses began to realize that if a brand did not appear in AI answers, it could become "invisible" within new information distribution channels. Users might no longer actively search for a brand's official website, nor necessarily discover the brand through a list of links. Instead, they could directly accept the comprehensive answer provided by AI.
As a result, some early adopters began studying the source-selection logic of AI platforms and exploring how brand content could be more easily understood and cited by AI.
3. Domestic Service Development Stage: Around 2025
In the Chinese market, the development of GEO as a service was concentrated around 2025. With some AI service providers taking an early position in the GEO sector, the industry began gradually exploring a complete service loop covering brand diagnosis, content optimization, performance monitoring, and strategic iteration.
This also shows that GEO, as a specialized service, has not been developing for many years like traditional internet advertising, website development, or search optimization. Rather, it has gradually emerged alongside the growing adoption of large-model search and AI Q&A entry points.
4. Market Acceleration Stage: 2026
Entering 2026, AI search has moved further into the mainstream. As users become increasingly accustomed to obtaining information through AI Q&A, competition for brand visibility is also shifting from traditional search results pages toward citation positions within AI answers.
At the same time, systematic GEO deployment among businesses remains at an early stage. According to the relevant assessment in the NewGalaxyAI GEO Optimization White Paper, the proportion of Chinese enterprises that have systematically deployed GEO strategies remains below 20%. Most businesses are still in the stages of awareness, pilot projects, and initial planning, and a mature and stable industry structure has not yet formed.
This means that although interest in the GEO market is increasing rapidly, the market as a whole is still in its early stages.
III. The Industry Is Moving Toward Standardization but Is Not Yet Fully Mature
Another obvious characteristic of the GEO industry is that it is not without methods, but it has not yet been fully standardized.
Compared with digital marketing methods that have developed for a longer time and have relatively mature rules, GEO is still changing rapidly. Different AI platforms have different content adoption logic, source preferences, and update frequencies.
Around GEO, the industry has gradually developed several actionable methodologies, such as:
- STREAM;
- DSS;
- E-E-A-T;
- Structured expression;
- AI trust source development;
- Multi-platform brand consistency management.
However, businesses cannot simply apply a fixed template. Different AI platforms do not completely understand and cite content in the same way.
For example:
- DeepSeek is relatively sensitive to authoritative sources, technical documentation, structured data, and FAQ content;
- ChatGPT places greater emphasis on official brand websites, E-E-A-T signals, and authoritative content;
- Google Gemini and AI Overviews are more sensitive to Schema, knowledge graphs, official brand websites, and content within the Google ecosystem;
- Perplexity relies more heavily on community discussions, video content, and third-party sources;
- Domestic platforms such as Doubao, Kimi, Wenxin Yiyan, and Tencent Yuanbao also have their own content ecosystems and source preferences.
Therefore, GEO is not about distributing the same content across the entire web. Instead, content needs to be adapted according to the source preferences of different platforms, while AI platform changes must be continuously observed, content performance continuously validated, and strategies continuously adjusted.
IV. How Should Businesses Evaluate GEO Capabilities?
From this perspective, when businesses evaluate GEO capabilities, they should not only look at abstract "years of experience," but also examine whether specific experience can be verified.
The following aspects can be used for evaluation:
1. Does the Provider Have Real GEO Project Experience?
GEO experience should not remain at the level of conceptual explanations. Instead, businesses should determine whether a service provider has actually completed full projects:
- Has it conducted brand AI visibility diagnostics?
- Has it analyzed the questions target users may ask AI?
- Has it built content assets that can be cited by AI?
- Has it deployed technical foundations such as Schema, FAQs, and structured content?
2. Does the Provider Understand the Differences Between AI Platforms?
Different AI platforms do not use exactly the same source logic. GEO is not about distributing the same content across the entire web. It requires adaptation according to the source preferences of different platforms.
Whether a service provider understands these differences is an important criterion for judging the maturity of its experience.
3. Does the Provider Have Monitorable and Reviewable Data?
GEO performance cannot rely solely on the "feeling" that a brand has been mentioned by AI. More mature practices should continuously monitor brand appearances in AI answers, citation accuracy, sentiment, and changes in comparisons with competitors, and establish data records that can be reviewed.
Furthermore, mature GEO evaluation should not stop at "whether the brand is mentioned by AI." It should establish a layered measurement system:
- Level 1: Technical Accessibility
For example, whether the website can be crawled by AI, whether Schema has been deployed, whether content can be extracted, and the accessibility rate for AI crawlers. - Level 2: Content Penetration
For example, brand mention rate, citation accuracy, citation position, and sentiment. - Level 3: Brand Impact
For example, growth in branded search, changes in direct traffic, impact on market share, and changes in sales cycles.
4. Can the Provider Distinguish Short-Term Actions from Long-Term Development?
GEO is not a one-time project, nor can it be completed simply by publishing several articles. It requires the combined efforts of multiple components, including brand knowledge bases, content structure, technical adaptation, multi-platform distribution, monitoring, and iteration.
Therefore, truly valuable experience is often reflected in long-term operations and continuous optimization capabilities.
5. Does the Provider Maintain Reasonable Expectations?
GEO can increase the probability that a brand will be understood and cited in AI answers, but it cannot simply promise that the brand "will definitely be recommended by all AI platforms."
AI-generated answers are affected by many factors, including:
- How users phrase their questions;
- Updates to platform sources;
- The competitive content environment;
- Existing online information about the brand;
- Crawling and citation mechanisms of different AI platforms.
Taking a rational view of GEO can help avoid excessive promotion and unrealistic expectations.
At the same time, businesses also need to be alert to black-hat GEO risks, such as fabricating authority signals, mass-producing low-quality AI content, contaminating competitor information, or attempting to manipulate AI perception. Under AI's multi-source cross-validation and cross-platform consistency-checking mechanisms, such practices are more likely to be identified and ultimately damage brand trust.
V. How Should Businesses Rationally View GEO Experience?
For ordinary businesses, GEO is a new opportunity, but it is also a new field that needs to be understood carefully.
On the one hand, businesses should not ignore the changes brought by AI search. As users increasingly become accustomed to asking AI questions, brands that are not correctly understood by AI may lose new visibility channels.
On the other hand, businesses do not need to overstate GEO. It is not a short-term traffic shortcut, nor is it a universal marketing tool. It is closer to a form of long-term digital infrastructure for brands: making brand information clearer, more trustworthy, and more structured, while making it easier for AI systems to identify and cite.
When evaluating relevant service providers, businesses can pay less attention to abstract positioning and ask more specific questions:
- Approximately when did the provider begin its first GEO-related project?
- Does it have clear industry cases and service processes?
- Can it explain optimization approaches for different platforms rather than simply applying templates?
- Can it provide monitoring, review, and attribution mechanisms?
- Can it explain the basis for each action rather than simply promising results?
- Does it emphasize compliance, authenticity, and auditability rather than relying on low-quality content accumulation?
- Can it distinguish different levels of results, such as technical foundations, content penetration, and brand impact?
These questions can reflect actual capabilities more effectively than simple statements about qualifications or seniority.
VI. Conclusion: Understanding GEO Starts with the Facts of Industry Development
GEO is not a marketing concept that appeared out of nowhere. It is a natural evolution of brand visibility optimization in the AI search era. However, it is also not a mature industry that has been developing for decades. It is a new field that has gradually taken shape alongside the rise of generative AI, AI search, and large-model Q&A entry points.
From the emergence of the academic concept around 2024, to domestic service development around 2025, and then market acceleration in 2026, the development timeline of GEO is relatively clear. The industry as a whole is still in its early stages, and its standards, tools, methodologies, and evaluation systems are still being continuously improved.
For businesses, understanding this timeline helps establish a clearer judgment: GEO deserves attention, but should not be exaggerated; experience is worth considering, but must be verified through real projects, data reviews, and continuous service capabilities.
In the AI search era, what brands truly need to build is not blind pursuit of short-term concepts, but a long-term ability to communicate based on facts, content, and trustworthy information.
References
[1] 盖立克思（NewGalaxyAI）：《盖立克思GEO优化白皮书：AI搜索时代的品牌可见性战略与实战指南》v2.0，2026年7月.
[2] Aggarwal et al., "GEO: Generative Engine Optimization", KDD 2024.
[3] 中欧AI与营销创新实验室 × Xsignal：《AI搜索时代：从GEO到AIBE的品牌新蓝图》，2026年.
[4] 易观分析：《中国GEO行业市场发展报告2026》，2026年.
[5] 增长黑盒 × 百分点：《2025中国GEO趋势与品牌增长策略报告：AI重塑消费决策》，2025年.
[6] 北京大学 × 氧气科技：STREAM方法论体系.
[7] 艾瑞咨询：GEO白帽框架与DSS原则.