5 Realistic Insights into Generative AI’s Impact on Search and SEO

As a technologist with a vested interest in the evolving landscape of search and AI, I’ve observed a lot of hype and misconceptions surrounding generative AI’s potential to revolutionise search engines and SEO. While I don’t claim to be an SEO specialist by any stretch, my experience at the intersection of technology and search media provides a unique vantage point to assess these developments realistically. Amidst a sea of overstatements and vague predictions, it’s crucial to ground ourselves in a realistic understanding of what’s truly happening. Here are five key points that shed light on the current state of generative AI and its genuine impact on search and SEO.

1. Google’s mission isn’t changing overnight 

For decades, Google has (along with other search engines) been fine-tuning its algorithms to connect users with the information they’re seeking. This relentless pursuit of relevancy and utility is unlikely to be derailed or revolutionised overnight by generative AI. While AI technologies offer new tools, Google’s core mission - to organise the world’s information and make it universally accessible and useful - remains steadfast. AI will enhance, not upend, the foundational principles that have made search so indispensable.  

Google has been very open about its objective to connect users to content that demonstrates E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness). This doesn’t change, because it is the goal, and not the mechanism. The convergence of AI and search will make the mechanism better, which will make EEAT even more important to marketers who wish to achieve visibility. 

2. AI acts as an augmented layer through inference 

AI often serves as an additional layer within the existing search retrieval process, primarily through a mechanism known as inference. Inference refers to how AI models interpret and convert conversational inputs into structured queries that can be processed by traditional search engines. 

• Bridging conversational inputs and search results: When users input conversational queries, AI models use inference to understand the intent and context. They then transform these queries into traditional search terms to retrieve relevant information. 
• Enhancing user experience: By effectively interpreting natural language, AI makes search more intuitive and user-friendly, reducing the need for users to formulate precise search terms. 

This process allows AI to augment traditional search mechanisms, providing richer, more contextualised responses without replacing the fundamental systems in place. 

3. LLMs and search engines serve different user goals 

Large language models (LLMs) like GPT-4 are powerful tools trained on vast datasets, but they are not inherently search engines. Understanding the distinction between the two is crucial, especially from the standpoint of user goals: 

• Users seeking direct answers: Some users are looking for immediate, concise answers to specific questions. Chatbots and LLMs excel at providing synthesised responses based on their training data, making them well-suited for these queries. 
• Users seeking sources and exploration: Other users aim to explore sources, destinations, or a range of options. Traditional search engines are designed to make underlying materials visible, allowing users to access and navigate to original content, websites, or documents. 

As AI and search technologies converge, these distinctions may progressively blur. AI-enhanced search engines are beginning to provide both direct answers and access to underlying sources. However, it’s important to recognise that search will always have the fundamental role of making the underlying materials accessible - because that’s what it’s designed to do. Even as AI becomes more integrated into search, the core function of making information visible remains essential. Users will continue to need access to original sources for verification, detailed exploration, and a broader understanding of topics. 

4. Implications as search-specific LLMs become mainstream 

As search-specific LLM-powered systems like Google’s Search Generative Experience (SGE), SearchGPT, and Perplexity gradually enter the mainstream, the SEO landscape will continue to evolve. This evolution is a natural progression, much like previous shifts in search technology. 

• Adapting optimisation strategies over time: Traditional SEO practices have always adapted to changes in search algorithms and user behaviour. With AI enhancing search, factors like content quality, relevance, and context become even more critical. Businesses can incrementally adjust their strategies to ensure their content remains comprehensive and authoritative, aligning with AI’s emphasis on quality. 
• Maintaining content visibility: While AI-generated answers may alter how information is presented, companies can focus on creating high-quality, AI-friendly content formats over time. This approach mirrors past adaptations to features like featured snippets and voice search, where gradual adjustments helped maintain visibility. 

Despite these developments, the foundational principles of SEO remain intact. Delivering valuable, relevant content to users continues to be the cornerstone of effective SEO. The integration of AI into search doesn’t upend these principles but rather reinforces them, highlighting the ongoing importance of quality and relevance. 

5. Practical challenges to ubiquitous AI-powered search 

The widespread adoption of generative AI in search faces significant hurdles, chief among them being computational feasibility. Executing every traditional search query through an LLM is an economic scalability nightmare. The costs per transaction for generative LLMs are exponentially higher than those for traditional database lookups. Concerns are mounting over the ability of LLM providers to scale economically, making it impractical to default to AI-driven search for every query at this stage. 

Navigating evolving expectations in search 

As generative AI continues to integrate into the search landscape, user experience and expectations will evolve. Users may gradually begin to appreciate more direct, synthesised answers alongside traditional search results, encouraging search engines to adapt their interfaces and technologies accordingly. However, this evolution aligns tightly with our core thesis: the foundational principles of delivering accurate and relevant information efficiently remain central to search. 

Businesses and users must stay informed and adaptable. While generative AI offers exciting new tools, recognising its potential and limitations allows for a measured approach. Practical challenges - particularly around scalability, cost, and the need to prevent AI-generated misinformation - mean that the integration of AI into search will be a thoughtful, progressive journey. 

By understanding these dynamics, businesses can navigate the changing landscape effectively, adjusting their strategies over time to remain visible and competitive in an AI-enhanced search environment.