Einblicke in die generative KI-Suchbranche
With the rapid development of generative AI technology, search engines are undergoing a profound transformation and gradually evolving into intelligent production tools. The rise of generative AI search has brought new growth space to the search engine industry. While traditional search engines rely heavily on keyword matching to show users a series of relevant links, generative AI search is disrupting this model. Not only does it have a deep understanding of semantics and context, but it can also directly generate accurate answers, providing users with an unprecedented and convenient search experience. In diesem Artikel, we will introduce the product form, technical principles, and market competition pattern of generative AI search, and analyze its future development trends and challenges.
Generative AI Search product overview
In the evolution of search engines, users have migrated from PC search to mobile APP search, and the current application of large model technology has transformed search into an open, generative intelligent Q&A and multi-round interactive process, which has significantly enhanced interactivity and intelligence. Traditional search engines have limitations in terms of result accuracy, user context understanding, real-time updates, and the application of generative AI technology. Entering the generative AI search stage, search is user-centric, focuses on accurately understanding search intent, and strives to achieve seamless end-to-end task processing, with functions such as semantic understanding, personalized recommendation, cross-modal and cross-language retrieval, and content generation.
The main types of generative AI search products include:
One is internet searching. Internet search is an important form of generative AI search engine, which mainly searches for massive public information on the Internet. Such search engines include upgraded versions of traditional search engines, such as Baidu (Baidu Search AI Discovery Edition), Microsoft (NewBing), Google (Bard). Gleichzeitig, it also covers some innovative AI search applications based on conversations, such as Perplexity AI and Myta AI, which continue to attract users through innovative features through deep integration of technology and user experience, and try to challenge the position of traditional search engines.
The second is embedded search in the platform. In-platform search is another common type of generative AI search engine. It usually exists as a functional module of the platform and is specifically designed to search for private data within the platform. The core advantage of this type of search engine is that it can use the large amount of user behavior data, preferences, historical search records and other information accumulated by the platform to provide users with customized search results. Zum Beispiel, Leonardo da Vinci of Xiaohongshu uses AI technology to analyze users’ preferences and needs, optimize search results, and provide accurate content recommendations.
The third is the internal search of the enterprise. Internal search is the embodiment of generative AI search engines in enterprise-level applications. It mainly deals with the internal data of the enterprise, such as unstructured data such as documents, emails, reports, usw. This data is often critical to business operations and decision-making, but due to its sheer volume and variety of formats, traditional search methods often struggle to extract and utilize this information effectively. Through the in-depth understanding and processing of these data, AI search can help employees extract the information they need from massive internal data more efficiently, thereby improving work efficiency and decision-making quality.
Core technical principles
The core technologies of generative AI search engines include natural language processing (NLP), deep learning, and knowledge graphs. These technologies enable AI to understand the semantics of queries, transcend the limitations of keyword matching, and provide users with more accurate answers through contextual association. It does this in several steps:
1. Understand query intent: Use natural language processing technology to accurately understand the intent of user queries and break through the limitations of traditional keyword matching.
2. Retrieving and processing data: By accessing a large number of data sources, combined with knowledge graphs or databases, relevant information can be quickly filtered out.
3. Generate answers: Combined with generative AI technology based on large models, summarize the results of the query and generate answers in natural language instead of a simple list of links.
4. Personalized recommendations: Customize personalized search results based on the user’s historical behavior, preferences, and context.
Generative AI search engines provide efficient and accurate search solutions by deeply integrating traditional search engines with AI semantic understanding technology, combining domain-specific data sources and index databases, and using large model generation capabilities, especially when dealing with complex queries. Its core competitiveness lies in the quality and quantity of data, and self-built index databases are essential to ensure the accuracy and timeliness of content, which is the key to improving the accuracy of generative AI searches.
Comparison between the traditional search process and the AI search process
The underlying mechanism of generative AI search is based on “Retrieval Enhanced Generation” (RAG), which combines the retrieval of traditional search engine APIs and self-built index databases, and uses large models to read and summarize content to directly provide user answers. Derzeit, generative AI search products mostly rely on traditional search engine APIs as Internet data support, but not all traditional search engines have open interfaces, and most startups use Bing’s external interfaces, such as Perplexity, Secret Tower, Chain Enterprises, usw., and domestic companies such as Baidu and 360 do not open API interfaces. Gleichzeitig, APIs such as generative large models such as ChatGPT are used for inference and generation, semantic understanding, triage, and process design of problems are carried out according to different business scenarios, and the most suitable size model for each scenario or process is selected for inference or generation, wie zum Beispiel 360 AI search has 9 large model calls. Most AI search startups will have some data sources and indexes in their own specific fields to increase their competitive differentiation. Zum Beispiel, Secret Tower AI’s podcasts and libraries, 360 has revamped the original search index database, usw.
Competitive landscape in the market
With the continuous development of artificial intelligence technology, “generative AI + search engine” has become a new track, and the competition is becoming increasingly fierce. A variety of products and applications have emerged in the search engine market, forming a vibrant industrial ecology. Each participant approaches from different levels and strives to occupy a place.
Traditional search engine vendors: By integrating AI technology to optimize the traditional search experience, and by virtue of their advantages in technology, Daten, and capital, they will expand their competitive advantages in the field of generative AI, and at the same time occupy an important position in the AI search market. Microsoft integrated ChatGPT with search engines to launch the “New Bing”, which for the first time demonstrated the application practice and development prospects of generative AI in the field of search. Baidu launched Wenxin Yiyan and integrated it into its search service.
Large model manufacturers: Entered the field of search with generative AI technology, launched tools that combine conversation and search, and relied on strong technical capabilities to provide core algorithm support for AI search. Zum Beispiel, OpenAI’s AI search tool SearchGPT can access information from the Internet in real time, aiming to provide users with more timely and accurate information. The Dark Side of the Moon launches the “Kimi Discovery Edition”, when users enter a keyword or a question to search, the main page displays AI-generated summary answers, and on the right side of the page is the “Web Search” column, which shows the source of the web page including images and AI reading.
Internet vendors: Relying on their deep application foundation and advantages, they have intensively deployed generative AI search, and many applications have launched services closely related to AI search. Zum Beispiel, Zhihu’s AI search product, Zhihu Direct Answer, launched a professional search function; Der “Intelligent Q&A” service is launched in the search bar of the Kuaishou APP, and AI helps users search and answer relevant questions. Even different departments of the same company are scrambling to launch their own AI search products. Zum Beispiel, ByteDance’s Douyin, Toutiao and Feishu explore different user needs and scenarios. Feishu has developed a local search engine to improve the convenience of users when looking for information, while Douyin e-commerce has optimized its shopping guide search function with the help of AI technology.
Startups: Rise with innovative user experience and rapid iteration capabilities, injecting new vitality into the search market and meeting personalized and professional needs. Zum Beispiel, Quark has received widespread attention and love from users for its simplified product design, one-stop service, and excellent performance in vertical segmentation scenarios. Vendors such as Perplexity have also carved out a niche in the AI search market through their unique technologies and product features.
The future of generative AI search
The explosion of AI technologies and applications has brought the generative AI search industry into a new stage of rapid development, and as innovative products continue to emerge, generative AI search is gradually reshaping the market landscape of traditional search engines. According to Gartner, von 2026, the number of visits to traditional search engines may decline by 25%, while the number of users of AI search products will grow rapidly, gradually approaching the user threshold of super apps. Gleichzeitig, the form of generative AI search products has been upgraded, and search engines are no longer limited to the role of information acquisition tools, but are transitioning to the form of integrated information processing products, and are committed to realizing a cross-modal search experience. In der Zukunft, generative AI search will integrate search, Integration, refinement, and creation into an all-round intelligent assistant and lead a new benchmark in the industry.
Although generative AI search engines show great market prospects, their development still faces many challenges. From a technical point of view, there is a significant gap between domestic products and similar foreign products in terms of technological maturity, originality and innovation ability. In terms of the market, the commercialization path is still being explored, and a mature business model has not yet been formed, coupled with the rapid growth of computing power demand, which has brought severe cost challenges. In terms of data, the lack of high-quality data acquisition and processing technology has become a key bottleneck restricting the further development of generative AI search technology. In the field of security, data privacy and security issues have attracted much attention from users, and issues such as the authority and accuracy of search results, user privacy protection, and content authenticity need to be solved urgently.
