SEO has evolved rapidly over the past five years, transforming how content is ranked and understood. This article explores the most important changes in tools, tactics, and algorithms, and how modern SEO compares to what came before.

Google: The Driving Force of SEO

I’m not much of a betting man, but if I were, I’d probably bet my house that you’re reading this article via a link you came across on Google. That’s because an organic viewer is 27 times more likely to come from Google than from the next closest search engine: Bing.

So, let’s start with what I believe is the most important SEO topic—Google itself. Google is, by far, the most advanced search engine and continues to lead the way with cutting-edge technology.

The Evolution of Google's AI and Machine Learning Algorithms

Then: Basic SEO

In the past, Google relied heavily on basic SEO strategies like:

Exact-match keywords
Backlinks
Simple on-page optimization

The core idea was to scan a page for words and phrases that matched the user’s query and pair it with the most relevant website. This often led to:

Low-quality, repetitive content
Keyword-stuffed pages
Sites focused more on ranking than on genuinely helping users

Backlink Focus

Backlinks were treated as a signal of authority, but quantity was often valued over quality. This approach made it easy to manipulate rankings with spammy links or irrelevant link-building schemes.

On-Page Optimization

Technical SEO was primarily structural. Tactics included:

Stuffing keywords into HTML elements like <title>, <meta>, <h1>, and alt tags
Minimal concern for user experience, mobile performance, or content value

Now: Smarter, Context-Aware SEO

Google has become significantly more intelligent in the last few years. Rather than playing catch-up in the AI movement, Google has led the charge, particularly in the SEO space. With advancements in machine learning, Google has transformed how it interprets search intent and content quality.

Let’s look at two of the biggest breakthroughs:

BERT (Bidirectional Encoder Representations from Transformers)

Launched: 2019

BERT was introduced to help Google understand the full context of words in a sentence—not just the keywords. Unlike older models that analyzed language from left to right, BERT processes the entire sentence at once, making it bidirectional.

Example:

Search query: "Can you get medicine for someone pharmacy?"

Before BERT: Google might focus on keywords like “medicine” and “pharmacy” and return general prescription-related results.
With BERT: Google understands the user is asking if they can pick up medicine on someone else’s behalf—a subtle but critical nuance.

SEO Impact:

Content that answers intent performs better than content that merely targets keywords.
Writing for humans instead of bots became essential.
Long-tail queries and natural language gained value.

MUM (Multitask Unified Model)

Launched: 2021

MUM takes BERT’s concept and supercharges it. It’s 1,000 times more powerful and is designed to be:

Multimodal: Understands text, images, audio, and video at the same time
Multilingual: Trained in 75+ languages and can transfer knowledge between them
Multitask: Can handle multiple tasks at once, including generating content and drawing conclusions from different sources

Example Use Case:

Query: "I’ve hiked Mt. Adams and now I want to hike Mt. Fuji next fall. What should I do differently?"

MUM can:

Compare the terrains, climates, gear needs, and physical demands
Pull relevant data from text, photos, and even foreign-language content