Google Search Traffic 'Stable' Amidst 148% Surge in ChatGPT Visits

A recent social media post from user @critter highlights a growing trend: individuals are increasingly turning to AI chatbots like ChatGPT for information previously sought through traditional search engines. The user tweeted, > "what app do you google with these days? I find myself googling a lot with chatgpt," reflecting a shift in how people access information online. This sentiment aligns with broader industry observations indicating a measurable impact of AI on established search behaviors.

Large Language Models (LLMs) like OpenAI's ChatGPT offer users direct, synthesized answers, reducing the "cognitive load" associated with sifting through multiple links from traditional search results. Experts note that LLMs are particularly useful for summarising documents, drafting, coding snippets, and exploratory queries. This personalized and conversational approach contrasts with the traditional search engine model, which often presents a list of links.

Despite the rapid adoption of AI chatbots, Google's global daily search visits remain largely stable, according to a Bank of America Global Research report from February 2025. The report indicated Google's visits were up 1% month-over-month to 2.7 billion in January, while ChatGPT visits surged 148% year-over-year to 128 million globally. While Google still commands approximately 90% of the global search market, AI chatbots are capturing a significant share of incremental "AI-driven activity."

The shift in behavior is most pronounced for "top-of-funnel" queries, where users seek definitions, explanations, comparisons, or how-to guides. Instead of navigating multiple websites, users are asking ChatGPT directly for answers to questions like "What is customer onboarding?" or "Best free tools for content planning." This directness means that while Google's overall volume remains high, the nature of queries it receives is evolving, with some users bypassing traditional search entirely for certain information needs.

Industry analysts predict a hybrid model for information retrieval, where LLMs and traditional search coexist, each serving different user needs. Companies are also adapting their digital strategies, focusing on "Generative Engine Optimization" (GEO) to ensure their content is accessible and useful for AI systems. This involves structuring content clearly, answering questions directly, and building strong E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) signals to be referenced in AI-generated summaries.