The Top AI Search Engines for 2025

Deciphering AI Search: Article #5 of 5

The realm of internet search technology is approaching a tipping point. While traditional search engines still rule the lion’s share of internet searches, search platforms powered by AI are poised to overtake the more conventional search engines in 2025.

As of late 2024, AI search engines have gained a significant foothold in the search market. AI search engines currently account for approximately 5-6% of the global search market, whereas Google has seen a decline in its market share, dropping from about 93% in 2023 to 91% in late 2024 (Search Engine Market Share, Oberla 2024).

While traditional search engines (particularly Google) remain in a dominant position, the market share numbers above represent a notable shift from previous years. And with Google, Bing, and other search engines integrating AI into their platforms, this trend is only going to accelerate. The lines between traditional and AI-powered search are blurring.

AI Search Engine Market Share

Here’s a breakdown of the current market share for AI search engines:

1. ChatGPT (excluding Copilot): 59.4%
2. Microsoft Copilot: 14.4%
3. Google Gemini (formerly Google Bard): 13.5%
4. Perplexity: 5.8%

Other AI search engines like Brave, Leo AI, Komo, Andi, You.com, and Phind are quickly catching up – despite each holding less than 2% market share, these upstarts are considered among the best AI search engines due to their innovative features and capabilities.

While AI search engines are still a relatively small part of the overall search market, that is likely to change, and soon. Many AI search platforms are seeing quarterly user growth rates between 6% and 20% (Top Generative AI Chatbots by Market Share – December 2024, FirstPageSage) suggesting their market share will continue to expand in the coming years.

Impact on Traditional Search Engines

Despite the rise of AI search engines, Google still maintains a dominant position in the global search market share. Don’t let this fool you, however – there are several key shifts in trends worth noting:

• AI-powered search results are becoming mainstream. Google’s AI Overviews, Bing’s generative search, and new entrants like Exa and Perplexity signify a fundamental shift towards AI-driven search experiences.
• Semantic understanding is key. Search engines are evolving beyond keyword matching, leveraging AI to understand the meaning and context of queries and content.
• Personalization and hyper-localization are growing. AI is enabling more tailored and geographically specific search results.
• Content strategies must adapt. SEO professionals need to optimize for AI algorithms, focusing on holistic content, unique storytelling, and expert insights.

The Rise of AI Overviews

AI-powered search results are becoming mainstream. Platforms like Google’s AI Overviews, Bing’s generative search, and new AI-driven entrants such as Exa and Perplexity are ushering in a fundamental shift towards AI-driven search experiences.

These systems rely on semantic understanding, moving beyond traditional keyword matching to interpret the meaning and context of queries and content more effectively, thereby providing accurate search results. At the same time, personalization and hyper-localization are growing trends, with AI enabling search results that are increasingly tailored to individual users’ needs and geographic locations.

This evolution necessitates content strategies that adapt to AI algorithms, focusing on holistic content, unique storytelling, and authoritative insights.

Google AI Overviews, for instance, have reached over 1 billion users across 100 countries, (though they remain unavailable in EU countries due to intellectual property concerns). Designed to provide concise summaries of complex topics or direct answers to simpler queries, Google’s AI Overviews enhance the search result experience, demonstrating the goal of making search more efficient.

Liz Reid, VP of Google Search, eloquently described this objective, stating, “Sometimes you want a quick answer, but you don’t have time to piece together all the information you need. Search will do the work for you with AI Overviews.”

For a deeper dive in Googles AI Overviews, be sure to check out our article, How Google AI Overviews Impact SEO.

AI Search Engines: The Major Players in 2025

1. Google Search with AI Overviews (AIOs):

What it does: Google's AI Overviews (AIOs) are integrated into its search results to offer concise, easily digestible summaries of information sourced from across the web. These overviews often appear prominently on the results page, designed to address user queries effectively without forcing searchers to dig through search results for answers.

How it works: AIOs analyze Google’s extensive knowledge graph and vast web index to produce answers that synthesize data from multiple reliable sources. These brief summaries are intended to save users time while permitting deeper exploration through links to original content.

Example Use Case: Searching for “healthy sleep habits” might yield a summary with essential tips, such as maintaining a consistent bedtime, reducing screen exposure before sleep, and creating a calming evening routine. The overview often includes links to trusted sources like the National Sleep Foundation or academic health sites for further details.

2. Microsoft Bing with Copilot:

What it does: Bing integrates OpenAI’s GPT-4 model for enriched search experiences, offering AI-generated insights alongside traditional search results. Unlike a typical AI search engine, which primarily focuses on advanced AI technologies and machine learning for query understanding and result accuracy, Bing combines these capabilities with conventional systems to provide a unique and flexible search experience. This dual approach enhances depth and usability, making it suitable for both casual and professional research.

How it works: Bing’s AI-powered results use natural language processing to understand user queries and generate tailored summaries, insights, and creative suggestions. These AI features are seamlessly integrated into search results, allowing users to toggle between expanded AI sections and standard search results.

Example Use Case: A traveller researching “top attractions in Tuscany” might find an AI overview covering historic cities like Florence and Siena, along with highlights such as the Leaning Tower of Pisa. Bing’s insights may also provide further tips on transportation, lodging, and cultural experiences, helping the user plan comprehensively.

3. Perplexity AI Search:

What it does: Perplexity AI focuses on delivering immediate and concise answers to complex questions by leveraging advanced natural language processing (NLP) and large language models. Perplexity AI includes image search capabilities to enhance user interaction, making the search experience more intuitive and personalized. It is designed to handle both straightforward queries and more nuanced, multi-layered inquiries, offering clarity and precision.

How it works: By utilizing an AI-driven approach, Perplexity AI searches across a broad range of reputable sources to provide direct answers alongside citations. Its model is particularly adept at understanding context, making it ideal for queries that might be ambiguous or open to interpretation. Transparency is key as Perplexity provides links to its sources, enabling users to verify information easily.

Example Use Case: A student asking, “What are the main differences between classical and operant conditioning in psychology?” could receive a brief yet informative answer summarizing points like the role of reinforcement and learning mechanisms. The response would include citations to trusted educational or academic sites, allowing for deeper study if necessary.

4. You.com:

What it does: You.com prioritizes a conversational approach, making it an ideal option for users who prefer chatbot-style interactions during their search process. Customization and privacy are key features of this platform.

How it works: Using natural language processing, You.com interprets queries intuitively and organizes results in an engaging dialogue format, unlike other search engines that typically present a list of links. It can retrieve data from diverse sources, including web pages, apps, and databases while ensuring user privacy is safeguarded.

Example Use Case: A food enthusiast seeking inspiration for recipes might type, “Show me a quick vegetarian pasta recipe with spinach and ricotta.” You.com would respond conversationally, offering multiple recipes with prep times, nutritional details, and direct links to cooking tutorials. Users can refine their preferences without initiating a brand-new query.

5. Brave Search:

What it does: Known for its strong commitment to user privacy, Brave Search emphasizes unbiased results by eliminating reliance on algorithms from major search engines like Google or Bing. Unlike a traditional search engine which integrates AI features while retaining classic functionalities, Brave Search focuses on delivering relevant information without compromising user data through its AI tools.

How it works: Brave Search relies on its independent index to pull data directly from sources while minimizing trackers and blocking intrusive ads. This ensures users have a fast, private, and ad-free experience without trade-offs in resulting quality.

Example Use Case: A privacy-conscious individual researching “top secure messaging apps” could rely on Brave Search to find unbiased information. Brave would deliver results drawn from privacy-focused resources, distinguishing its platform as an excellent choice for users concerned about data security.

6. Exa AI:

What it does: Designed specifically for developers and AI creators, Exa AI functions as a highly specialized search engine for sourcing information-rich content. It is particularly beneficial for training AI systems and machine learning models.

How it works: Unlike traditional search engines, Exa AI employs vector databases and embedding models to evaluate and rank the relevance of web pages. It carefully selects high-value content while filtering out misleading SEO-optimized pages and low-quality AI-generated text.

Example Use Case: A developer working on an AI assistant for scientific research could use Exa AI to gather peer-reviewed scientific articles and credible datasets. The search engine would filter out unnecessary or low-quality pages, ensuring the selected information supports accurate and reliable AI outputs.

These AI search engines demonstrate the diverse ways AI is transforming online search and information retrieval. Each platform offers unique features and capabilities catering to specific user needs and preferences. As AI technology continues to evolve, we can expect even more innovative and user-centric search experiences in the future.

The Future of AI-Powered Search

Many (if not all) of these AI-powered search options were unheard of even one short year ago. The advancements in AI search engines and semantic understanding have come at a blazing pace, marking a pivotal shift in how we access and interact with information. And these advancements are just the beginning.

AI Search and Semantic Understanding

Semantic understanding plays a crucial role in various natural language processing (NLP) tasks. By leveraging word embeddings (which represent complex relationships between words in high-dimensional space) AI models can now analyze text, generate content, and comprehend user intent more effectively, thereby providing accurate search results.

This conversion of text into numerical form enables machines to process and understand human language. Additionally, cosine similarity (a mathematical way to measure how similar two sets of information are by calculating the cosine of the angle between them) is increasingly being employed to intelligently assess the similarity between embeddings, aiding search engines in retrieving relevant content. By capturing semantic meaning and relationships, embeddings enhance the comparison of longer text segments and improve semantic search capabilities.

Word Embeddings in AI and NLP

Word embeddings are a fundamental component of modern natural language processing (NLP) and play a significant role in enabling AI systems to understand and process human language effectively. At their core, embeddings are vector representations of text – such as words, phrases, or entire sentences – that capture semantic meaning and relationships in a high-dimensional space.

This transformation of textual information into numerical form allows AI systems to work with language in a mathematically structured way, uncovering hidden patterns and syntactic or semantic similarities between words.

A Final Word… for Now

Whether for casual browsing, academic research, or AI development, the possibilities of AI-powered search are seemingly endless. As innovation continues to reshape these systems, the future of search isn’t just about finding answers – it’s about empowering knowledge and creating personalized digital experiences tailored to every individual’s needs.

The era of smarter, seamless, and more intuitive searches has arrived, and it’s only getting started. By familiarizing yourself with these new technologies now, you can ensure you searches are fruitful and your content remains visible, accessible, and valuable long into the future.