Background
I was born in 1990, the same year that the first search engine, Archie, was born. When I was in elementary school, I was always excited to find the latest information from Lycos (or Ask Jeeves in the later years). Consequently, in sixth grade, I took first place in the Search Information Competition.
When I conduct research using a search engine, whether it's market research for a company or academic research at graduate school, people who work with me frequently ask how I get the data and reports and turn them into insights. In today's terms, it's as if I had a built-in AI deep search system.
In this article, I try to break down user behavior and understand how AI fits into the overall picture.
Key Numbers
- Approximately 16.4 billion searches are conducted on Google every day
- Google maintains an 80% dominance in this space, though its leadership faces challenges from AI-native alternatives.
- Consumer adoption of AI-powered search is projected to surge by 2028, with 79% of users intending to adopt and over 70% trusting AI-generated results.
Chain of Thought
Why do questions matter?
I remember when I was a child, my mom bought me the entire I Wonder Why children's book series. Then she expressed regret that I had asked ten questions for each (think how many questions I asked her in total). Some people's main purpose for seeking information is to make decisions and take action. For someone else, like me, to explore creativity while learning about the world around us.
The questions you ask and the problems you focus on shape the world you leave behind.
What's the current path to information?
People seek information to help them make decisions in a variety of situations, including professional use such as the workplace, school, or personal life.
Workplace
In the workplace, information seeking is typically purpose-driven, with the goal of solving specific problems or improving job performance. Often use an internal database, knowledge-based search engine, and collaboration tools.
Academic
Information seeking is learning-focused and motivated by academic objectives like finishing projects, carrying out research, or increasing knowledge in schools and universities. Typically, use the library, learning management systems like Moodle and Google Classroom, and academic databases.
Personal
In general, people seek information based on their interests, hobbies, or daily needs. Search engines, social media and forums, and personal networks such as word-of-mouth are examples of diverse sources.
People prefer different values under different situations. For example, the source and accuracy of information are crucial in the workplace and in academia. Response time and accessibility may be more significant for personal use. The values drive our decision to use AI to help us consume information.
What to do with information?
The methods of providing information have evolved throughout time, from the internet to mobile phones to the AI era. From text to audio and video. From Twitter and TikTok to ChatGPT. The context length varies depending on the type of information and influences how we absorb it and rely on the source.
The main problems we faced in the past years may have been data fragmentation, quality, and overload. How we process, store, and use information shapes our behavior and decision-making.
I previously had a knowledge management hub (or knowledge base) in Evernote, Notion, and Heptabase. Basically, the steps I took to organize the information were similar:
Select
For me, searching is the easiest step; however, selecting the information I want from various channels is the challenge. Google is not always the first pick; it depends on the situation at hand. Some of my friends ask questions on social media to get answers. They are not just looking for information; rather, their friends are assisting them in filtering and selecting information in advance.
Attention is all you need.
This is similar to the brain's attention bottleneck, which involves filtering out noise to focus on what is relevant. How to (effectively) select and filter depends on one's experience. We did it ourselves in the past, and in the age of social media, we outsourced. AI may now be able to do this for us.
Record
Once I had the information I needed, I categorized it into different categories. This is the stage where I will digest the information and convert it into personal knowledge. Sometimes I discover insights, but most of the time I just save them and hope to connect with other notes someday to turn them into new insights.
Retrieve
Most of the time, when I need to retrieve information or knowledge, it is during a mentor session, public sharing, or posting on social media. I like to know who my audience is so that I can deliver it in a way that they will like and find easier to consume. Friends used to describe my speaking style as robot-like (not the first time). Then I work hard to recall my old skill: telling a story. I'm glad that now I can speak in different tones to different people.
How AI bridges gaps on both backend and frontend?
There are tons of search engines that have exploded in popularity in recent years. In this paragraph, I want to break down into two parts: how search engines find information (the backend, or infrastructure) and how AI presents and delivers information to users (the frontend, or interface).
Frontend
Morphic, an open-source search engine, was the one that I used frequently. I tried many, and none of them solved my search experience. I prefer visual results to plain text, as well as deep context length over rapid and brief answers (before deep research came out). However, if you prefer quick and short answers, Perplexity is your best option.
From an interface viewpoint, not just the chat UI and follow-up questions provide users a new way to search. The power of AI in interface is also reflected in AI-generated UI, like Flowith. The browsing experience is entirely different than before. Instead of simply showing link retrieval, the above gives a new answer-generating experience.
Backend
When I first started researching this market, I wondered if we should build an AI-powered search engine to do what so many search engines did in the 1990s, such as ranking, indexing, and so on. Based on my knowledge of retrieval mechanics and my experience developing RAG, I understand that the power of neural search comes from semantic understanding via embeddings.
AI here bridges the gap by capturing context and meaning, leading to more relevant results even when there's no exact keyword (the traditional method) match. Given the high cost (compute and storage) of neural search, what factors drive the tradeoff? What types of content and user queries are best suited for semantic indexing?
Keyword search (with LLMs) remains effective for precise names, unique identities, and a clear structure of information. While neural search works best when the user does not know the specific keywords, it also works well with rich, complex content such as knowledge bases, legal or technical documents, and user-generated content.
Glean is a major player in the enterprise knowledge base market, Harvey is mentioned frequently in legal documents, and Elicit has recently caught the attention of many researchers. Which market segment should neural search focus on right now to balance the tradeoffs?
Think outside the box
I learned from a startup founder who is developing the agent that he has yet to determine the ideal UX for human-AI interaction. I believe it is similar in this field as well. By reviewing the CoT of this post, as a user and developer, what I expect is a new way to get information. Search engines are only one aspect, and they are also somewhat outdated, as shown by the history I mentioned earlier.
On the other hand, if you're a Marvel fan, you can't ignore the intelligent assistant Jarvis. Now, we still interact by typing and reading in a search box. In my perspective, search engines are merely one form of engagement that isn't really interesting. People's behavior changes dramatically as they consume information, from Google to Facebook to TikTok. This generation may benefit from multi-dimensional experiences that include both visual and audio components.
I'm looking forward to more immersive and interactive experiences for users seeking information in the future.
Citation
Yi-Han, Liu. (Mar 2025). "What's Missing in Today's Search Engines? Exploring the Role of AI". Hannie Liu. https://www.hannieliu.com/mind/250331-search-engine
References
[1] Chang, S., & Chang, S. (2024, September 8). AI strategies for B2B and B2C: A Comparative Study in Marketing. RapidLeadsPro. https://rapidleadspro.com/ai-digital-marketing/ai-strategies-for-b2b-and-b2c/
[2] Discover thousands of collaborative articles on 2500+ skills. (n.d.). https://www.linkedin.com/pulse/ai-enterprise-search-market-size-share-growth-drivers-bklnf/
[3] D’Souza, A., & Singh, R. (2023). Global API Marketplace Market Size, Share & Industry Trends Analysis Report by component (Platform and Services), by organization size, by end user, by regional outlook and forecast, 2023 - 2030. In KBV Research. https://www.kbvresearch.com/api-marketplace-market/
[4] IBISWorld, Inc. (n.d.). IBISWorld - industry market research, reports, and statistics. Copyright © 1999-2025 IBISWorld, Inc. https://www.ibisworld.com/united-states/market-size/search-engines/1982/
[5] Lown, P. (2025, January 7). Social media sentiment analysis: Benefits and guide for 2025. Sprout Social. https://sproutsocial.com/insights/social-media-sentiment-analysis/
[6] Madsen, K. (2025, January 7). Will AI grow bigger than Google search? 2020-2028 prediction. Morningscore. https://morningscore.io/will-ai-grow-bigger-than-google-search-2020-2028-statistics-and-my-predictions/
[7] Şanlı, F. (2024, September 3). Social Media Sentiment Analysis: Understanding Consumers. AlternaCX. https://alternacx.com/blog/social-media-sentiment-analysis-understanding-consumers/
[8] Search engine market share worldwide | StatCounter Global Stats. (n.d.). StatCounter Global Stats. https://gs.statcounter.com/search-engine-market-share