https://www.techtarget.com/whatis/feature/GenAI-search-vs-traditional-search-engines-How-they-differ
In the early days of the internet, it became clear that some form of technology was needed to help users navigate and find information as traffic began to surge. This led to the emergence of the first search engines, which helped users find content on the growing internet.
For most of the history of the internet, search has been predicated on the same basic process: Content is indexed to match keyword queries. With the advent of generative AI, the search landscape began to shift in a meaningful way for the first time in decades. GenAI search enables users to get results that include more and are not limited to keyword relevance. With GenAI search, large language models (LLMs) provide answers instead of only a page of results.
Multiple providers have entered the GenAI search market in recent years. Startups such as Perplexity AI have come into the space with entirely new GenAI search services. OpenAI, the vendor behind ChatGPT, entered the space with its ChatGPT search. Existing search vendors, including Google and Microsoft, have also offered AI options. Google has its AI Overviews technology, and Microsoft Bing has also integrated GenAI augmented search. Meta is also reportedly exploring GenAI search for its social media platforms.
While GenAI search is still a relatively new concept, it has experienced rapid adoption. A Statista and SEMrush report found that one in 10 U.S. internet users employ genAI tools for online search. Approximately 112.6 million people in the U.S. used AI-powered tools in 2024, and that figure is forecast to rise to 241 million by 2027.
Google users also routinely experience AI-generated summaries. A Pew Research report found that 58% of surveyed Google users got an AI-generated summary when conducting a search query.
GenAI search engines are an evolution of search engine technology.
The primary defining innovation and characteristic of GenAI search engines is the use of LLMs to deliver a different type of experience than a traditional search engine. A GenAI search engine uses the LLM to understand, retrieve and generate responses to user queries. A regular LLM generates answers to user queries based on the knowledge available up to its training cutoff date, limiting its ability to provide information beyond that point.
In contrast, a GenAI search engine has no such knowledge cutoff and can use up-to-date information, similar to a regular search engine, to generate responses. GenAI search engines combine machine learning and natural language processing to understand user queries semantically.
The use of an LLM provides a series of different benefits and capabilities, including the following:
GenAI searches work similarly to how an LLM normally works but with some notable distinctions -- most significantly, the incorporation of updated information.
The basic procedures involved in GenAI searches include the following:
Traditional search engines, such as Google's core search index, operate on a different principle compared to GenAI search engines. Traditional search engines help users find the best answers to their queries by offering links to relevant sources.
The basic process behind how traditional search engines work includes the following:
There are numerous key differences between GenAI search and traditional search engine approaches.
| Aspect | GenAI search engines | Traditional search engines |
| Response | Direct, conversational responses | List of links with snippets |
| Content generation | Can create original content on the fly | Only retrieve existing information |
| Query understanding | Advanced understanding of natural language and user intent | Primarily keyword-based with some semantic understanding |
| Contextual awareness | Maintain context throughout a conversation | Has limited context awareness -- each query treated separately |
| Information synthesis | Can combine information from multiple sources | Present separate results from different sources |
| Update frequency | Can incorporate recent information | Depend on web crawling and indexing cycles |
| Personalization | Can tailor responses based on conversation history | Personalize based on user data and search history |
Beyond search engines providing AI capabilities, multiple web browsers have also directly incorporated AI features.
The integration of AI into traditional web browsers is transforming user experiences, enhancing functionality and in some cases addressing privacy concerns.
The Apple Safari web browser integrates AI capabilities via Apple Intelligence. As of Safari 18, the primary AI feature is web page summaries, using the reader view.
The Brave web browser -- originally created by ex-Mozilla staffers -- integrates its own AI assistant. The Leo AI Assistant can help users to summarize, translate and analyze web content.
The DuckDuckGo browser is taking a privacy-focused approach to AI integration. The company also builds its own search engine, which is tightly integrated with the web browser. Key AI features in the DuckDuckGo browser include the following:
Google Chrome is one of the most used web browsers in the world. In addition to enabling AI Overviews in Google Search, Chrome has integrated several AI features to enhance browsing. These include the following:
Microsoft Edge -- commonly installed with Microsoft Windows -- also benefits from integrated AI capabilities. Current features include the following:
Mozilla Firefox -- an open source browser that has its roots in the Netscape web browser -- has also integrated AI capabilities to improve UX. Capabilities include the following:
GenAI search technologies offer neatly summarized answers without the need to click through pages of search results. However, that convenience comes with some accuracy and potential risk of misinformation.
Columbia University's Tow Center for Digital Journalism report found AI tools provided incorrect answers to more than 60% of queries, with error rates ranging from 37% (Perplexity) to 94% (Grok 3).
Even when chatbots identified articles correctly, they often failed to link to original sources, instead citing syndicated versions on platforms such as Yahoo News. Adding further insult to injury, some AI tools -- particularly Grok 3 and Gemini -- frequently provided broken or fabricated URLs that led to error pages rather than actual articles.
The University of Washington's Center for an Informed Public has warned that GenAI could make search less reliable overall. The concern from the report is that AI-powered search engines may prioritize confident-sounding responses over factually accurate ones, potentially amplifying misinformation at scale.
For nearly as long as search engines have existed, individuals and organizations have focused on search engine optimization. SEO attempts to improve a webpage and its content to achieve a higher ranking in search results.
The rise of AI-powered search engines is impacting SEO strategies in significant ways, including the following:
Another effect of GenAI search is the emergence of generative engine optimization (GEO).
Instead of just optimizing for traditional search with SEO, GEO aims to optimize content for AI models to be cited or summarized.
GEO complements SEO, focusing on visibility in platforms such as ChatGPT, Google AI Overviews and Perplexity.
Emerging best practices for GEO include the following:
Traditional search engines have been on the internet for over two decades. Over that time frame, the traditional search engines have iterated many times to keep delivering the best results for users. The path for search and AI will likely be the same with continued evolution and iteration.
As these technologies continue to evolve, the market should expect to see the following:
Sean Michael Kerner is an IT consultant, technology enthusiast and tinkerer. He has pulled Token Ring, configured NetWare and been known to compile his own Linux kernel. He consults with industry and media organizations on technology issues.
08 Aug 2025