Recently, there’s been a lot of chatter in the SEO community about how traditional SEO techniques are becoming obsolete. Some experts claim that AI-powered search engines are taking over, and soon, everything we know about SEO will be irrelevant. But is this really true, or is it just another buzz phrase like “SEO is dead,” which we’ve been hearing for years? Let’s break it down and see what’s really going on.

What is Traditional SEO?

Before we dive into the AI stuff, let’s quickly recap what traditional SEO is all about. Traditional SEO refers to the strategies and techniques used to optimize websites for index-based search engines like Google, Bing, Baidu (in China), or Yandex (in Russia). These techniques are broadly divided into three categories:

1. On-Page SEO: This involves optimizing individual web pages for better visibility. Think clean navigation, easy readability, keyword placement, meta tags, and internal linking. For example, if you’re writing a blog about “best cameras for vlogging,” you’d ensure the keyword appears naturally in the title, headers, and content.
2. Off-Page SEO: This is all about building your website’s authority and relevance outside of your own domain. Techniques include backlinks, social signals, directory listings, and mentions on other reputable sites. For instance, if a popular tech blog links to your camera review, it signals to search engines that your content is trustworthy.
3. Technical SEO: This focuses on the backend of your website to ensure it’s crawlable, fast, and mobile-friendly. Think improving site speed, fixing broken links, enabling HTTPS, and ensuring your site renders well on all devices.

If you’ve been in the SEO game for a while, you’re probably familiar with these basics. But here’s the twist: with the rise of AI search engines, the rules of the game are evolving. Let’s explore how.

How Do AI Search Engines Work?

AI search engines, like those powered by large language models (LLMs), operate on a fundamentally different approach compared to traditional search engines. During my time working with a team to figure out how to optimize for AI engines, I discovered that they rely on three core principles:

1. Pre-Training Dataset: AI models are trained on massive datasets that include text from books, websites, and other sources. This helps them understand language patterns and context.
2. Natural Language Processing (NLP): NLP allows AI engines to interpret the intent behind a query, even if it’s phrased in a conversational way. For example, if you ask, “What’s a good camera for a beginner vlogger?” the AI understands you’re looking for affordable, user-friendly options.
3. Web Search: Interestingly, most AI engines still rely on traditional search engines for real-time data. Even though some AI models have their own crawlers (like OpenAI’s SearchBot or PerplexityBot), they’re not as advanced as Google’s crawlers yet. So, they often pull data from existing search indexes.

How Do AI Engines Process Queries?

Here’s where things get fascinating. With AI search engines, users don’t type in short keywords like “best vlogging camera.” Instead, they ask full-sentence questions like, “I’m Deepak, and I want to start vlogging. Can you suggest a stable and affordable camera?”

When you submit a query, the AI engine extracts key phrases, understands the context and intent, and then performs a web search. It analyzes the results using its pre-trained dataset and NLP capabilities to provide a precise answer. 

For example, when I asked SearchGPT, “I want to become a vlogger. Can you suggest a stable and cheap camera?” It gave me a list of cameras with links to buy.

But here’s the kicker: when I asked how it searched the web, it revealed that it used keywords like “best budget vlogging cameras with stabilization in India.” So, even though the user’s query was conversational, the AI still relied on traditional keyword-based searches to fetch results.

What Factors Influence AI Search Results?

Just like traditional SEO, AI search engines consider hundreds of factors when ranking content. However, the emphasis has shifted slightly. Here are the primary factors:

1. Search Intent and User Needs

AI engines prioritize understanding why someone is searching. For instance, if you ask, “Should I upgrade to the iPhone 16?”, the AI doesn’t just look for specs. It analyzes intent: Are you a budget-conscious buyer? A tech enthusiast?

It then hunts for content that addresses those unspoken needs, like a comparison article titled “iPhone 16 vs. iPhone 15: Is the Upgrade Worth It for Casual Users?” or a Reddit thread discussing battery life issues. Content that solves problems clearly and directly—without fluff—wins here.

2. E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness)

Google’s E-E-A-T framework has become even more critical with AI. Let’s say you’re writing about “How to Fix iPhone Overheating Issues.” An AI engine will favor a guide written by a certified Apple technician (expertise) over a generic blog post. If Wired or The Verge cites your article (authoritativeness), that’s a golden ticket.

Trustworthiness also matters: Is your site secure (HTTPS)? Do you cite sources? For example, a tech blog sharing verified iOS update logs from Apple’s official site will outrank vague, unsourced claims.

3. Backlinks and Brand Authority

Backlinks aren’t dead, but their role has shifted. AI engines still use links to gauge credibility, but they’re smarter about context. Imagine you run a small gadget review site. A single backlink from CNET or TechRadar (e.g., “10 Best Smartphones of 2024”) holds far more weight than dozens of spammy directory links.

Even unlinked brand mentions matter. If a YouTube tech influencer says, “I read on GadgetZone that the iPhone 16’s camera struggles in low light,” AI may still associate your site with authority.

4. User Engagement Signals

AI pays attention to how real humans interact with your content. Say you publish a detailed guide like “How to Extend iPhone Battery Life.” If readers spend 10 minutes on the page, click internal links, or share it on X (Twitter), AI interprets this as a thumbs-up.

Conversely, if your “2024 Tech Trends” article has a 90% bounce rate, AI assumes it’s irrelevant. Social signals matter too: A viral TikTok video dissecting your iPhone review can indirectly boost your SEO, as AI correlates social buzz with credibility.

5. Technical SEO Basics

Even the smartest AI can’t rank your site if it’s technically broken. For example, a slow-loading blog about the latest Android updates will lose to a competitor with better Core Web Vitals.

Mobile-friendliness is non-negotiable, AI knows most searches happen on phones. JavaScript-heavy sites (like interactive tech demos) risk being misunderstood by AI crawlers; server-side rendering ensures your content is parsed correctly. Structured data (schema markup) also helps AI “read” your content. Tagging product prices or FAQs in a “Best Gaming Phones” roundup makes it easier for AI to extract answers.

6. Content Freshness and Depth

AI favors content that’s both current and thorough. A 2023 article titled “Why Foldable Phones Are Flopping” won’t cut it in 2025 if Samsung’s Galaxy Z Fold 6 is selling out. Update it with new sales data, user reviews, and competitor analysis. Depth matters too: A 300-word listicle on “Top Smartwatches” can’t compete with a 2,000-word guide comparing battery life, app ecosystems, and fitness tracking accuracy across brands like Apple, Garmin, and Fitbit. In short, don’t just publish new content, focus on updating the old content frequently.

7. Semantic Relevance and Natural Language

AI understands context, not just static keywords. If you’re writing about “AI in smartphones,” don’t just repeat “AI in smartphones” 50 times. Instead, discuss related terms like machine learning cameras, voice assistant improvements, or privacy concerns with on-device AI.

For example, a post titled “How Google’s Gemini AI Integrates with Pixel Phones” would naturally include phrases like “real-time translation,” “adaptive battery management,” and “ethical AI use.” Write how people talk—conversational queries like “Is the iPhone 15 worth buying in 2024?” demand casual, jargon-free answers.

How to Optimize for AI Search Engines

Now, the million-dollar question: how do you optimize your website for AI search engines? The good news is, many traditional SEO techniques still apply, but with a few tweaks:

Focus on Authority and Mentions: 

AI engines value credibility. Aim to get mentions from authoritative sites like news platforms, industry blogs, and local directories. For example, if Crunchwiser is mentioned (even without any link back to the website) in a tech magazine or a well-known AI blog, it boosts your chances of appearing in AI search results.

Optimize for Crawlability: 

AI search agents often struggle with JavaScript-heavy websites. To ensure your site is crawlable, consider using dynamic rendering, server-side rendering, or static rendering. Google recommends these techniques, and they work well for AI engines too.

Create Conversational Content: 

Since users are shifting from keyword-based searches to conversational queries, your content should reflect this change. For instance, instead of targeting “best vlogging camera,” create content that answers questions like, “What’s the best camera for a beginner vlogger on a budget?”

Leverage Structured Data: 

Structured data helps search engines understand your content better. Use schema markup to highlight key information, such as product details, reviews, or FAQs.

Conclusion: Is Traditional SEO Dead in 2025?

Not quite. While AI search engines are changing the game, many traditional SEO principles still hold value. The key difference lies in how users interact with search engines and what they expect from the results. User behavior has shifted from keyword-based searches to conversational queries, and SEO deliverables now include not just rankings but also direct answers and recommendations.

For example, if you’re optimizing for SearchGPT, Perplexity or latest Deepseek search, you’d still focus on building authority, creating high-quality content, and ensuring technical excellence. But you’d also adapt to the conversational nature of AI searches by answering questions like, “How does Deepseek compare to other AI tools?” or “What are the best use cases for ChatGPT Reason?”

In short, SEO in 2025 isn’t dead, it’s evolving. And as always, staying ahead of the curve means understanding these changes and adapting your strategy accordingly. So, keep optimizing, keep experimenting, and remember: the core of SEO, providing value to users, remains the same, no matter how the technology evolves.