Google’s evolution from RankBrain to BERT and now MUM represents a seismic shift in how search understands content. These AI models have progressively moved from simple keyword interpretation to true semantic understanding — focusing on intent, relationships, and meaning rather than exact phrases.
Google’s algorithms no longer read; they comprehend.
If you want to future-proof your SEO, understanding how these systems differ — and how they build on one another — is essential.
The Evolution of Google’s AI Systems
Since 2015, Google’s AI updates have all shared one goal: to help search understand users like humans do. Each model built upon the last, deepening semantic comprehension and contextual relevance.
| Algorithm | Year Introduced | Core Function | Key Advancement |
|---|---|---|---|
| RankBrain | 2015 | Machine learning ranking algorithm | Interprets unseen queries and user intent |
| BERT | 2019 | Natural Language Processing (NLP) model | Understands context and relationships between words |
| MUM | 2021 | Multitask Unified Model | Understands meaning across languages, media, and intent |
Together, these models form the backbone of Google’s “AI-first search era.”
For a closer look at the latest evolution, read Google MUM Explained: How Multitask Unified Model Understands Content.
What Is RankBrain?
RankBrain → interprets → user intent behind unfamiliar queries.
Launched in 2015, RankBrain was Google’s first major step into machine learning. It helped Google process queries it had never seen before by identifying patterns and synonyms.
Instead of matching exact keywords, RankBrain analysed:
- Semantic similarity between words.
- User engagement data (clicks, bounce rate, dwell time).
- Query reformulations and context.
For example, when someone searched “best shoes for standing all day”, RankBrain understood it meant “comfortable work shoes,” even if that phrase didn’t appear verbatim.
RankBrain taught Google to interpret intent, not just text.
How to Optimise for RankBrain
- Focus on natural language and conversational phrasing.
- Write content that solves problems, not just targets keywords.
- Improve user signals — engagement and time on page matter.
To learn more about intent mapping, explore Search Intent Optimisation.
Is RankBrain still relevant today?
Yes. While newer models like BERT and MUM have surpassed it in complexity, RankBrain remains part of Google’s core ranking systems, particularly for understanding unseen or ambiguous queries.
What Is BERT?
BERT (Bidirectional Encoder Representations from Transformers) → understands → the relationship between words within context.
Introduced in 2019, BERT allowed Google to read language both forward and backward (“bidirectionally”). This revolutionised how search engines process meaning — especially for prepositions, modifiers, and question phrasing.
Example:
Before BERT, the query “can you get medicine for someone pharmacy” might ignore the word “for.” After BERT, Google understood the full context — that the query is about picking up a prescription on behalf of someone else.
BERT improved:
- Natural language understanding (NLP).
- Featured snippet accuracy.
- Question-answering performance.
BERT brought nuance, empathy, and grammar to Google’s comprehension.
How to Optimise for BERT
- Write conversationally — think how people ask questions aloud.
- Target long-tail queries that express clear intent.
- Provide concise answers supported by context and structure.
If you’ve structured your site using a pillar-cluster model, you’re already aligning with BERT’s focus on context. Learn more in Content Frameworks: Hub and Spoke, Pillar-Cluster Models.
How does BERT affect SEO content writing?
It encourages clarity and flow. Google can now detect when a sentence is overly complex or keyword-stuffed — rewarding content that’s easy to read and contextually precise.
What Is MUM?
MUM (Multitask Unified Model) → synthesises → information across languages and formats to answer complex questions.
Released in 2021, MUM is 1,000 times more powerful than BERT and designed to process text, images, video, and audio together. It represents Google’s transition into true multimodal understanding.
MUM can:
- Translate knowledge across 75+ languages.
- Connect multiple search intents into one response.
- Interpret entities, relationships, and contextual meaning across content types.
Example:
A query like “I’ve hiked Mount Adams — will Mount Fuji be harder?” requires cross-language, cross-topic reasoning. MUM can infer terrain difficulty, climate, and preparation tips — synthesising insights from multiple sources.
MUM is Google’s bridge to generative AI search, powering AI Overviews (SGE).
How to Optimise for MUM
- Use multimodal content (text, video, images, schema).
- Strengthen E-E-A-T signals — experience and credibility matter most.
- Build entity networks through precise internal linking.
- Update content regularly to maintain freshness and factual accuracy.
You can learn how MUM powers generative search in AI Overviews Optimisation: How to Get Featured in Google SGE.
Does MUM replace BERT and RankBrain?
No — MUM complements them. All three systems coexist. RankBrain interprets patterns, BERT handles natural language, and MUM connects meaning across formats and languages.
RankBrain vs BERT vs MUM: Key Differences
| Feature | RankBrain | BERT | MUM |
|---|---|---|---|
| Launch Year | 2015 | 2019 | 2021 |
| Primary Goal | Interpret unseen queries | Understand language context | Connect multi-format, multilingual meaning |
| Core Technology | Machine learning | Transformer (NLP) | Multimodal AI |
| Content Type | Text-based | Text-based | Text, image, audio, video |
| Optimisation Focus | Intent + engagement | Context + syntax | Entities + meaning |
| Example Query | “Best shoes for flat feet” | “How to get medicine for someone else” | “Is Mount Fuji harder to climb than Mount Adams?” |
Each system adds a new layer to Google’s ability to think like a human — RankBrain predicts, BERT understands, and MUM connects.
Together, they represent the evolution from keyword search to cognitive search.
The Combined Impact on SEO
Because all three systems operate simultaneously, SEO must adapt to a multi-layered environment.
- RankBrain rewards user engagement.
- BERT rewards natural readability.
- MUM rewards semantic depth and multimodal optimisation.
This means your content strategy should:
- Cover topics comprehensively within clusters.
- Optimise for intent and entities, not just phrases.
- Include visuals, structured data, and author authority.
To measure the success of these signals, use your Performance Metrics Framework.
Search is now an ecosystem — and your content must evolve with it.
How These Models Support E-E-A-T
E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) → guides → which content Google’s AI systems trust most.
All three algorithms use E-E-A-T to validate meaning and relevance:
- RankBrain learns from user behaviour (trust through engagement).
- BERT values clarity and expert-written explanations.
- MUM prioritises verified experience and multimodal authenticity.
Writers who blend expertise with structured, evidence-based storytelling will outperform those relying solely on automation. See how to strengthen these signals in E-E-A-T for Content Writers.
Conclusion
The evolution from RankBrain → BERT → MUM shows Google’s trajectory from matching words to understanding the world.
Modern SEO is no longer about “writing for search engines” — it’s about helping Google’s AI interpret your meaning clearly and confidently.
By focusing on entities, intent, and experience, you align your content with how search systems truly understand language today.
Next step: Audit your content for contextual depth, entity coverage, and multimodal opportunities using the Content Auditing Framework.
