SEO Insights in Numbers: The Evolving Relationship Between AI and Traditional Google Search

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By Gloria Braunsberger, SEO & Online Strategy

The rise of AI-powered search tools has created a more diverse information ecosystem rather than a winner-takes-all competition with traditional search engines. Despite predictions of Google's demise, traditional search engines still dominate with roughly 90% market share while AI tools collectively hold about 6% as of early 2025. Google's search volume actually increased by 21.64% in 2024 compared to 2023. Instead of replacement, we're seeing users develop sophisticated platform-switching behaviors, using different tools for different query types—traditional search for quick facts and local information, AI tools for complex explanations and creative tasks.

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Traditional search engines maintain overwhelming dominance in raw usage numbers despite impressive growth from AI competitors. Google's position remains remarkably strong with approximately 89.62% market share in early 2025 [9, 11, 12], down only slightly from 92-93% in 2020 [9, 13]. This relative stability comes despite an explosion of AI alternatives during this period. These five years have seen Google's user base expand from roughly 4 billion monthly active users in 2023 to 5.01 billion in 2025 [18].

Meanwhile, Bing has shown modest but steady growth, increasing from about 2.5-3% market share in 2020 to approximately 4.01% by early 2025 [11, 15, 17]. This improvement was driven largely by Microsoft's early and aggressive integration of OpenAI's technology into Bing, rebranded as Microsoft Copilot [15, 70].

Search volume data reveals the truly massive gap between traditional and AI search:

• Google processes approximately 13.7 billion searches daily (420 billion monthly) [6, 7]

• ChatGPT handles roughly 37.5 million search-like prompts daily [6, 20]

• Even if all ChatGPT usage were search-related (it isn't), Google would still process about 373 times more queries [6, 7]

AI search tools have shown remarkable growth from virtually zero market presence in 2020:

• ChatGPT: From 1 million users in first 5 days (Nov 2022) to 400 million weekly active users (Feb 2025) [20, 21]

• Claude: Approximately 18.9 million monthly active users (early 2025) [22, 88]

• Perplexity: 15 million monthly active users (early 2025) [23, 25]

• DeepSeek: 33.7 million monthly active users (Jan 2025) [27, 28]

• Gemini: 350 million monthly active users (March 2025) [29, 81]

However, usage patterns differ dramatically between platforms. The average Google user performs about 135 searches monthly, while the average ChatGPT user has just 1.5 conversations per month [6, 14].

From keywords to conversations: The evolution of search behavior

The most significant transformation across the search landscape has been the shift from keyword-based to natural language queries.

Traditional search in 2020 was dominated by short keywords averaging 3-4 words per query, with users adapting their language to simplified "computer speak" to get better results [31, 37, 38]. By 2025, natural language queries have become prevalent across all platforms, with dramatically different query formats:

• Average Google search: 4.2 words

• Average ChatGPT prompt: 23 words

This shift toward conversational search has been accelerated by:

• Voice search adoption: 46% of Americans now use voice assistants, with 8.4 billion digital voice assistants in use globally by 2025 [32, 36]

• Generational differences: 82% of Gen Z users have adopted natural language search patterns compared to 45% of Baby Boomers [3, 90]

• AI integration in traditional search: Google's AI Overviews and Gemini integration have encouraged more natural queries [45, 47, 74]

User expectations have evolved alongside these behavioral changes. In 2020, users expected to navigate through multiple links to gather information. By 2025, 54% expect direct answers within the search interface itself [53, 89].

A key finding is that 99% of AI platform users continue to use traditional search engines, while only 16.45% of traditional search engine users also use AI platforms [3, 44]. This indicates AI tools are supplementing rather than replacing traditional search.

When each platform shines: Context-switching between search tools

Users are developing increasingly sophisticated context-switching behaviors based on query type.

AI tools are preferred for:

• Complex, multi-part questions requiring synthesis [54, 82]

• Learning and educational content (24% report improved research skills) [24, 35]

• Creative ideation and brainstorming [35, 69]

• Personalized recommendations (27% prefer chatbots for recommendation searches) [3, 14]

• Code and technical assistance [41, 49]

Traditional search remains dominant for:

• Quick factual information retrieval [14, 67]

• Local business searches (70% of local searches on Google) [32, 90]

• Initial product research (42% prefer search engines) [44, 54]

• Visual searches and shopping [3, 90]

• News and recent developments (56.8% trust traditional search more) [39, 57]

This bifurcation of use cases suggests a future where search tools become more specialized rather than converging on a single model [30, 43].

New metrics for a new era: How we measure search effectiveness

The rise of AI search tools is disrupting traditional SEO metrics and creating the need for new measurement approaches.

Click-Through Rate (CTR) has been significantly impacted as AI tools provide direct answers without requiring users to visit websites:

• Google's AI Overviews caused a 34.5% drop in position 1 CTR [58, 59]

• Organic CTR fell from 1.41% to 0.64% when AI Overviews appeared (2024-2025) [60, 61]

• Featured snippets have a 42.9% CTR, while AI Overviews have 38.9% [58, 62]

Session duration shows dramatic differences between platforms:

• Perplexity AI: Average session duration of 10 minutes and 51 seconds [64, 65]

• Traditional search sessions: Majority under 1 minute [53, 90]

• Users completing the same tasks were 158% more productive with ChatGPT than Google [54, 67]

Source citation practices vary significantly:

• ChatGPT includes the most links per response (10.42 on average) [57, 69]

• Google AI Overviews include 9.26 links per response [57, 58]

• Perplexity includes 5.01 links per response [66, 69]

• Bing Copilot includes 3.13 links per response [69, 70]

New metrics specific to AI search have emerged, including hallucination rate (factual accuracy), cross-domain authority (integration of information across subjects), and follow-up query success (ability to maintain context) [71, 72].

Recent developments and where we're headed

The most recent period (2024-2025) has seen both traditional search engines and AI tools evolve toward each other:

Traditional search platforms have rapidly integrated AI capabilities:

• Google's AI Overviews launched widely in May 2024, now reaching over 1 billion people [45, 73]

• Gemini 2.0 integration in Google Search in early 2025 [74, 75]

• Microsoft's continued integration of ChatGPT into Bing [81, 82]

AI platforms have enhanced their search capabilities:

• ChatGPT Search launched in October 2024 [84, 86]

• Perplexity positioned explicitly as an AI search engine replacement [64, 65]

• DeepSeek emerged rapidly, particularly in Asian markets [27, 28]

Rather than a winner-takes-all competition, we're seeing a diversification of the search ecosystem with platforms optimizing for different use cases. The AI search market is expected to grow from approximately 6% in 2024 to 14% by 2028 [5, 94], while traditional search will continue to evolve by incorporating AI capabilities.

Conclusion

The evolution of search from 2020 to 2025 represents not a revolution but a diversification. Traditional search engines maintain dominance in market share, while AI tools have created new modalities of information retrieval that excel in specific contexts. Users have developed increasingly sophisticated platform selection behaviors, choosing different tools based on query types and personal preferences.

The most significant shift has been toward natural language, conversational search across all platforms. This represents a fundamental change in how users interact with information systems—computers now understand human language better, rather than humans adapting to computer limitations [95, 96, 97].

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Further Work

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