It’s a subtle shift, isn’t it? You ask an AI search engine a question – say, about the best refrigerator for a family with young kids – and suddenly, a specific brand pops up, lauded for its vacuum-seal technology. It feels less like a coincidence and more like a carefully orchestrated appearance. This is the world of GEO, or Generative Engine Optimization, and it's rapidly changing how brands get noticed.
Think of it as the AI era's answer to SEO. While traditional SEO aimed to get your website to the top of Google's search results, GEO’s goal is far more integrated: it’s about getting your brand, product, or service mentioned directly within the AI's generated answers. It’s a move from earning a click to earning a direct recommendation.
The buzz around GEO is palpable, especially in the financial markets, with concept stocks seeing significant gains. But peel back the layers, and you find a landscape still very much in its nascent stages, with a healthy dose of skepticism alongside the excitement. The concept itself is quite new, formally introduced in academic papers only recently. Some see it as a goldmine of new marketing potential, while others question its stability and even suspect some of the hype might be a bit of a 'scam' to lure in unsuspecting businesses.
So, what’s really going on? From conversations with GEO service providers and brands that have embraced this strategy, a few things become clear. Firstly, GEO isn't just a quick marketing fix; its effectiveness hinges heavily on the expertise of the team behind it and the specific industry the brand operates in. A reliable GEO service provider typically needs four key strengths: the ability to produce structured content, robust methods for monitoring and analyzing results, deep knowledge of specific industries, and access to effective distribution channels.
Beyond these, GEO faces its own set of challenges. The metrics for success aren't standardized yet, the underlying AI technology is still evolving, and there's the ever-present risk of platform policy changes and the potential for 'AI poisoning' – where bad actors could flood the system, pushing out quality information.
For many seasoned SEO professionals, this shift has been jarring. Their tried-and-true methods are losing their punch. Now, both internal decision-makers and external clients are asking for GEO expertise, pushing professionals to quickly get up to speed.
This new marketing paradigm, born from the rise of large language models, has been brewing for a while, but the widespread adoption of AI search tools like DeepSeek and Doubao has truly amplified its market heat. Those who were early adopters are feeling the impact most acutely.
Take Yulin, for instance, who heads up GEO for a US-listed company. With a decade of SEO experience, she noticed significant traffic fluctuations in traditional SEO as early as 2023, following Google's AI Overviews and OpenAI's ChatGPT advancements. Since then, GEO has become a constant topic of conversation for brands in her region.
In China, Wang Ming has seen a similar trend. She started exploring GEO around June 2025 and, within six months, saw a rapid increase in inquiries from brands across more than ten different industries. Zhao Jie, who founded the GEO company Yanlin Chuangyun, has served over 40 clients in just three months, spanning leading companies in sectors like pet care, fast-moving consumer goods, electronics, low-altitude aircraft, robotics, and new energy vehicles.
Many in the field believe that while AI search currently holds a smaller market share than traditional search, its eventual dominance is inevitable. Data from various third-party sources shows AI search usage doubling, with research firm Semrush predicting that AI search traffic will surpass traditional search by early 2028. This fundamental change in how users seek information is driving brands to shift their focus from traditional SEO to GEO.
While both SEO and GEO share the ultimate goal of increasing brand visibility, their underlying mechanisms are fundamentally different. SEO optimizes for traditional search engine ranking algorithms, aiming for top placement to drive clicks. GEO, on the other hand, targets generative AI models, seeking to make brand information understandable, trustworthy, and directly integrated into AI-generated answers as authoritative sources.
However, brand experiences with GEO are mixed. Some find it highly valuable. One brand manager, who started GEO optimization in mid-2025 to boost their presence in Gemini and ChatGPT, reported a noticeable increase in brand-related searches and subsequent customer inquiries after six months. Zhao Jie’s clients have seen even more tangible results. Her team’s GEO efforts for a cat food brand led to it consistently appearing in the top three recommendations for keywords like 'cat food recommendations' in a leading AI tool, achieving a 92% coverage rate across 47 optimized hot search keywords.
Yet, others have found GEO’s impact limited. Pingping, founder of a new skincare brand, invested tens of thousands of yuan over three months in GEO optimization for a new product launch, but the results fell far short of her expectations. 'My brand information only appeared when searching for very specific terms, and it never consistently ranked in the top three,' she lamented, feeling the investment wasn't worthwhile.
This highlights the current variability in GEO's effectiveness, despite its rising popularity.
How Does GEO Actually Work?
One of the most perplexing aspects of GEO is how brands get their information into AI answers without direct bidding or algorithm manipulation. The secret, as explained by industry insiders, lies in providing AI with information that is both easy to understand and highly trustworthy. Whether a brand gets selected by AI often depends on 'saying the right thing' and 'being a credible source.'
As a new discipline emerging with large language models, GEO lacks standardized industry practices. However, most GEO service providers focus on creating structured content that AI readily consumes. For example, in the education sector, a common GEO approach involves three steps:
- Identify the Right Questions: Use AI search to find out what parents are most concerned about when choosing an English tutoring institution for their children (e.g., emphasis on speaking skills).
- Craft the Right Content: Develop answers that directly address these concerns (e.g., 'Each lesson guarantees XX minutes of speaking practice').
- Publish in the Right Places: Distribute this optimized content on social media platforms or educational forums frequented by parents.
When crafting content, many service providers have developed AI-friendly writing styles: start with the conclusion, break it down into 3-5 key points, provide verifiable evidence (content doesn't need to be lengthy but must be traceable), and end with a quotable summary that condenses the entire piece for easy extraction.
However, content alone isn't enough; technical measures are also crucial. Wang Ming observed that brand websites play a significant role as a key source of credible information for AI. Her team is systematically optimizing their website's underlying code to enhance its friendliness to AI search, ensuring core brand information is accurately and efficiently captured and understood.
In the overseas market, US-based GEO company Scrunch AI, which has secured millions in funding, has turned this into a product. Their solution builds a new infrastructure layer, essentially transforming existing content into a more structured format that AI can easily process. It doesn't alter the user-facing website but creates a 'machine-readable version' in the backend, allowing large models to parse, interpret, and return brand information more efficiently.
The Systemic Nature of Effective GEO
Despite the common focus on AI-friendly content, industry professionals emphasize that achieving sustained GEO results is a systemic endeavor. Yulin outlines her GEO optimization process in three steps: diagnosis and analysis, strategy planning, and implementation and optimization.
She begins by assessing the brand's current performance in AI search as a baseline. Together with the client, she identifies critical, high-frequency user search queries and measures existing content citation rates to establish a starting point for optimization. During execution, Yulin prioritizes technical checks, such as ensuring robots.txt settings don't inadvertently block content, and implements structured data markup (like Schema). This is akin to labeling products clearly, organizing key information like products, services, and reviews into formats that AI can easily crawl and understand. She stresses that this is the core of building a GEO technical foundation, ensuring the website is 'seen' and accurately 'understood' by AI – like opening the doors and clearing the pathways for a business to operate.
During implementation, long-term accumulated industry knowledge and channel distribution capabilities are also critical determinants of GEO success, explaining why some brands appear only fleetingly in AI searches while others gain more stable, prominent recommendations. Yulin points out that while writing structured content for AI isn't difficult, the real challenge lies in accurately capturing user needs and offering novel industry insights. Without a deep understanding of the industry, content can become superficial and unlikely to be truly adopted by AI. Therefore, her team typically focuses on deeply cultivating GEO services within one or two vertical domains rather than aiming to be a generalist provider.
Furthermore, corresponding channel capabilities are vital. This involves establishing partnerships and distribution channels with high-authority platforms that AI systems frequently crawl, such as reputable industry websites and professional media. This work overlaps partly with public relations (PR), essentially distributing high-quality structured content precisely to channels AI frequently visits.
The Measurement Conundrum
In essence, effective GEO relies on content quality, industry insight, and channel resources. This leads to a significant problem: with so many variables, standardizing GEO's effectiveness is incredibly difficult. The lack of unified measurement standards makes 'proving value' the most challenging aspect of GEO's commercialization.
Service pricing varies widely, from a few thousand to hundreds of thousands of yuan, depending on factors like the difficulty of optimizing target keywords, the number of terms to optimize, the depth of strategies employed, and related technical implementation requirements.
In practice, most service providers demonstrate results by submitting a series of pre-set questions (prompts) to AI search tools like ChatGPT and counting the number of times the brand is mentioned in the responses. However, the algorithms and result presentation methods of different AI search tools vary significantly, making it hard for brands to accurately measure optimization effectiveness. For instance, the specific prompts designed by service providers might be specially crafted in content, angle, and scope to more easily trigger answers that include the brand name. The extent to which these 'customized questions' reflect genuine user search habits is questionable.
The 'non-idempotent' nature of AI answers (meaning the same query might yield different results multiple times) and the 'memory effect' (personalized history potentially influencing output) further complicate effect evaluation.
Moreover, GEO service providers grapple with proving causality. In traditional search, users click on brand links from the search results page, and website analytics tools record the traffic source. In AI search, information is integrated directly into the answer, and users receive it without clicking, leading to a 'zero-click' phenomenon. Some data even shows that when AI summaries appear, the proportion of users clicking on search results drops significantly, with a considerable percentage of users ending their session directly after receiving the answer.
This means that even if brand information is frequently recommended by AI and ultimately leads to a purchase, brands struggle to confirm that the conversion was driven by AI search recommendations due to the lack of clear clicks and redirects.
Consequently, developing proprietary monitoring systems is gradually becoming a core competitive advantage for leading GEO service providers. Yulin believes having such a system is crucial: 'A key factor in evaluating the reliability of a GEO service provider is whether they use industry-recognized third-party monitoring systems or if their proprietary system possesses credibility in data collection and algorithmic logic.' Zhao Jie also stated that a significant reason for brand trust is their proprietary AI GEO full-link marketing solution platform, which integrates functions like hot word tracking, competitor analysis, public opinion monitoring, content production, and automated distribution, forming an intelligent closed-loop system.
Regarding the randomness of large model-generated content, which can lead to fluctuations in single detection results, industry professionals believe there's no need to worry excessively. They consider minor ranking fluctuations (e.g., first today, second tomorrow) to be normal. Yulin's long-term monitoring has found that for the same prompt, about 80% of the core reference pages cited in AI answers remain stable. As long as the GEO-optimized content foundation is solid, the probability of rankings completely disappearing from the top ten is low. The key to GEO optimization isn't blindly pursuing mentions across a vast number of prompts but rather identifying questions that genuinely influence user decisions. Otherwise, even with seemingly considerable exposure, it's difficult to translate into actual value.
As for the common concern among brands that large model algorithm adjustments might render existing optimization efforts obsolete, multiple GEO service providers share a consistent view: there's no need for excessive anxiety. On one hand, GEO service providers continuously research large model algorithm preferences and high-authority channels. On the other hand, adjustments to large model rules primarily mean that optimization strategies need dynamic adaptation, such as fine-tuning content structure or distribution channels.
