Evolving from technology-centric search to 'understanding and citing search'

Search is no longer simply a tool for finding information. Today's search engines have evolved into intelligent systems that interpret users' query intent, select and summarize reliable information, and sometimes even provide answers directly. Especially with the proliferation of generative AI, the meaning of being exposed in search results has expanded beyond "attracting clicks" to "becoming information that AI and search engines can trust and cite." Failure to understand this shift will inevitably lead to companies and brands' content being relegated to the margins of search results.

Key Trends in Google's Search Algorithm

Since its launch in 1998, Google Search has consistently evolved toward "better answers." However, the standards for doing so have changed over time.

1) The era of keyword-based search (1998–2010)

Early search algorithms relied heavily on keyword frequency and the number of links. Key ranking criteria were the frequency of a specific keyword being repeated on a page and the number of external links received. During this period, SEO exploits like keyword stuffing and link farming were prevalent, and the quality of search results was inconsistent.

2) The rise of content quality and context (2011–2014)

With the Panda and Penguin updates, low-quality content and artificial link manipulation began to be actively punished. Content that was actually helpful, rather than simply listing keywords, began to appear at the top. From this point on, search engines shifted their focus from "quantity" to "quality."

3) Semantic Search and User Intent Interpretation (2015–2018)

The introduction of RankBrain marked a turning point in the search paradigm. Google began to interpret search intent beyond keywords themselves, evolving its algorithms to understand context and meaning. This marked the beginning of a major shift in query-based and natural language search.

4) Structuring Trust and Expertise (2019–2022)

The EEAT (Experience, Expertise, Authoritativeness, Trustworthiness) concept is gaining traction, with questions like "Who wrote this?" and "Is this information trustworthy?" becoming key evaluation factors. In particular, in the YMYL (Your Money or Your Life) category, the official nature of the company or institution, authorship information, and clarity of sources are beginning to directly impact rankings.

5) The era of AI-based understanding and citation (2023–present)

Recent search is moving beyond simple "finding" to "summarizing and citing." In the AI Overview and generative summaries space, structured data, clear paragraph structure, and question-and-answer formats are prioritized. At this stage, search engine optimization expands beyond SEO into the realms of Answer Engine Optimization (AEO) and AI Citation Optimization.

The Impact of Algorithm Changes on Businesses and Brands

The shift in Google's search algorithm demands a fundamental shift in content strategy. Now, content that ranks high isn't simply "searchable," but must meet the following criteria:

• A structure that clearly addresses search intent
• Information that reveals the expertise and responsibility of the company or writer
• Paragraph structure and logical flow that are easy for AI to understand
• Content in a form that can be reused, quoted, and summarized

In particular, the reliability of website content as an official information archive is becoming increasingly important.

Search Optimization Strategies to Adapt to Change

Amidst these changes, the strategies that companies and brands must adopt are clear.

First, content must be restructured around questions and answers rather than keywords.

Second, content design needs to be structured by information units rather than by page units.

Third, trust signals such as author, company, source, and update history must be clearly exposed.

Fourth, structured data (schema), a clear heading system, and summarizeable paragraph structure become essential elements.

Search is no longer a 'competition for exposure' but a 'competition for trust'.

Adaptive Search Algorithm Methods as Reference Cases

While blog-centric SEO content was initially effective, AI summarization is increasingly embracing official information, such as corporate newsrooms, FAQs, policy pages, and report archives. Especially in multilingual environments, content that reflects regional context and legal and cultural norms, rather than simple translations, is valued more highly.

Summary of Key Insights from Google's Search Algorithm Changes

The evolution of Google search is less about technological change and more about the evolution of how trust is determined. In the past, keywords were the answer, then content quality was the answer, and now, understandable and quotable information is the answer. In the future, search engine optimization will no longer be a matter of tricks, but rather of information design and communication strategy.