Google's AI Mode: How Natural Language and Multimodal Queries Are Reshaping Search

The US Surge in Search Behavior Shifts Driven by AI Mode

With the introduction of Google's AI Mode, dramatic changes are underway in search behavior among US users. According to Shivani Mohan, Vice President of Data Science and UXR at Google, AI Mode is not only altering how people search but also expanding the definition of what is searchable.

Since its US launch a year ago (as of May 19, 2026), AI Mode has surpassed 1 billion monthly active users worldwide. Moreover, queries through AI Mode have been growing at a pace of more than doubling each quarter since launch, highlighting its rapid adoption.

From Keyword Search to Natural Language Dialogue

What developers should pay most attention to is the evolving nature of search queries. There's a clear shift from traditional keyword-based searches toward more specific, context-aware natural language queries.

1. Longer and More Specific Queries

According to Google's data, the average length of search queries in AI Mode is three times that of traditional searches. This indicates users are moving away from single keywords to natural language inquiries that convey detailed intent and context in sentences.

2. Evolving Use Cases: Planning and Brainstorming

Users are leveraging AI Mode not just for information retrieval but as a thinking partner. Key trends include:

• Planning: Queries related to planning have increased by 80% in the last six months, outpacing AI Mode's overall growth rate.
• Brainstorming: Since launch, brainstorming-related queries have been growing 30% faster than the overall average.
• Usage Phrases: Phrases like "where to," "where should I," and "ideas for" are on the rise, reflecting demand for suggestions and plans.

Explosive Growth of Multimodal Search

Beyond text-based interactions, search experiences utilizing images and audio are becoming mainstream. In the US, more than one in every six searches now involves images or audio, with image search particularly seeing month-over-month growth of over 40%.

Implications for RAG and AI SEO Developers

These trends offer crucial insights for next-generation search optimization and RAG (Retrieval-Augmented Generation) implementation.

First, users are submitting longer, more specific queries. Optimization for semantic search and advanced vector search becomes increasingly important over traditional keyword matching. Second, rising demand for generative tasks like planning and idea generation calls for structured data that can provide step-by-step suggestions and alternatives rather than single answers.

Additionally, the surge in image search underscores the growing importance of building multimodal indexes, not just text-based ones.