What is MUM?

Google’s Multitask Unified Model (MUM) is a powerful AI. It aims to improve how the search engine understands information. Essentially, MUM helps Google process complex questions more like a human. This reduces the need for you to perform many searches. The technology is built on the T5 framework. It is reportedly 1,000 times more powerful than its predecessor, BERT.

For SEO professionals, MUM is a critical evolution. It represents a major shift in how Google works. Its power comes from three key features. It is multimodal, meaning it understands text, images, and video together. It is multilingual, trained on over 75 languages to share knowledge between them. It is also a multitasker, handling complex questions in a single attempt. This change requires SEOs to think beyond keywords. They must now focus on topical authority and multimedia content.

MUM changes how Google answers layered questions. For example, imagine you hiked Mt. Adams. You now want to hike Mt. Fuji next year. MUM understands the implied comparison in your query. It can process your English question and analyze a Japanese blog about Mt. Fuji. It can also interpret images of hiking gear. MUM then combines this data to give you one complete answer.

How SEO Professionals Must Adapt

• Shift from single keywords to building deep topical authority.
• Create content that includes a mix of text, video, and images.
• Focus heavily on signals of Expertise, Authoritativeness, and Trustworthiness (E-A-T).
• Embrace international SEO to compete on a global level.

The Generational Leap: From BERT to MUM

To grasp the importance of MUM, we must first look at BERT. This comparison shows a big change in Google’s technology. It also reveals Google’s ambition for the future of search.

Recap: The Role of BERT

BERT was a landmark algorithm introduced in 2019. Its main innovation was understanding the context of words in a sentence. It looked at words both before and after a term. This helped Google understand natural language nuances. However, BERT had limits. It focused mainly on text. It struggled with complex queries and could not interpret images or video well.

The MUM Revolution

MUM is built on a more advanced system called the T5 framework. This is a major step forward. It treats all language tasks as a “text-to-text” problem. The model takes a text input and creates a text output. This allows MUM not only to understand language but also to generate it.

This ability powers features like AI Overviews. BERT helped Google find a better webpage for your query. MUM can create a new, unique answer from many sources. This turns Google from a “search engine” into an “answer engine.” More user questions may be answered without a click to a website.

Table 1: BERT vs. MUM: A Comparative Analysis

Attribute	Google BERT (2019)	Google MUM (2021)
Core Technology	Transformer-based neural network	T5 (Text-to-Text Transformer) framework
Primary Function	Language Understanding	Information Understanding & Generation
Key Capability	Bidirectional Context	Multitask, Multimodal, Multilingual
Power Level	Baseline	1,000x more powerful
Modalities	Text-only	Text, Images, Video, Audio
Language Capability	Primarily English-focused	Trained on 75+ languages

Beyond Language: How MUM Processes World Knowledge

MUM’s training gives it a deep understanding of world knowledge. It sees relationships between real-world things. These include people, places, and concepts. This allows it to reason. It can synthesize data to answer complex questions. It knows “Mt. Fuji” and “Mt. Adams” are both mountains. Therefore, it can compare them on things like trail difficulty and climate.

Deconstructing MUM’s Core Powers

MUM’s strength comes from three pillars. These are multimodality, multilingualism, and multitasking. These three “M’s” create a more intuitive search experience.

Multimodality: Unifying Text, Images, and Video

Multimodality is MUM’s ability to process different formats at once. It can understand webpages, images, and videos together. This allows for new kinds of search interactions. For instance, you could take a photo of your hiking boots. Then you could ask, “can I use these to hike Mt. Fuji?”

MUM processes this query by:

1. Visually identifying the hiking boots in the image.
2. Connecting the image to your text question.
3. Using its knowledge about hiking and Mt. Fuji’s terrain.
4. Giving a helpful answer, perhaps suggesting waterproof gear.

This power is integrated with tools like Google Lens. It makes visual search more conversational and useful.

Multilingualism: Breaking Down Language Barriers

MUM was trained across 75+ languages simultaneously. This allows it to transfer knowledge between them with ease. This is not simple translation. It is cross-lingual knowledge transfer.

If you search in English for info on Mt. Fuji, MUM might find the best content on a Japanese blog. It can understand the insights from that source. Then, it can present them in the English search results. This changes the SEO landscape. You are no longer competing only with English websites. You now compete with the most authoritative content globally, regardless of language.

Multitasking: Solving Complex Journeys in a Single Query

Multitasking is MUM’s ability to handle layered questions. Google says these queries once took an average of eight searches to answer.

Let’s revisit the hiker query. “I’ve hiked Mt. Adams and want to hike Mt. Fuji next fall, what should I do differently?”

MUM deconstructs this into multiple hidden tasks:

• Identify and Compare: It knows to compare Mt. Adams and Mt. Fuji.
• Understand Context: It sees you have experience and need specific advice.
• Synthesize Sub-Topics: It gathers data on trail conditions, weather, gear, and training.
• Deliver a Comprehensive Result: It builds a complete answer that anticipates your follow-up questions.

The New SEO Playbook: Adapting for a MUM-Powered World

Understanding MUM is the first step. The next is to create an actionable strategy. SEO professionals need a new playbook that moves beyond old tactics.

The Paradigm Shift: From Keywords to Topical Authority

Keywords still matter for understanding user demand. However, the main focus must shift. You should now aim to build comprehensive topical authority. This is also known as “entity-based SEO.” You optimize for concepts and their relationships, not just text strings.

For a query like “CRM for small business,” MUM sees related topics. These include “lead management” and “sales automation.” To succeed, you should create content hubs or topic clusters. These structures cover a core subject from every angle. This proves your site is an expert source.

Content Strategy Reimagined: The Multimedia Imperative

In a world with MUM, a text-only strategy is a major liability. MUM’s multimodal nature means that videos and images are no longer extra assets. They are core components of a complete content piece.

When creating content, ask, “What is the best medium to explain this?” The answer is often a mix of formats. A single article should ideally embed videos. Use well-labeled charts with descriptive alt text. Provide transcripts for audio and video. This makes your entire content ecosystem accessible to search engines.

Reinforcing Trust: Why E-A-T is the Cornerstone

Google will rely more on sources with high Expertise, Authoritativeness, and Trustworthiness (E-A-T). As MUM breaks down language and format barriers, E-A-T becomes the universal standard of quality.

To succeed, you must make your E-A-T signals clear. Showcase author expertise with detailed biographies. Get backlinks from reputable sites. Clearly source all data and claims. Ensure your content is accurate and regularly updated.

Technical SEO in Focus: The Role of Structured Data

Structured data (Schema.org markup) is still a vital tool. While MUM is powerful, schema provides clear signals to search engines. It helps them categorize your content with perfect accuracy. This is less about helping Google understand your content. It is more about helping Google feature it. Using specific schema makes your content eligible for rich results. Technical SEO is evolving from ensuring “understandability” to enabling strategic “feature-ability.”

Field Guide: Common Mistakes and Best Practices

Adapting to MUM means embracing new strategies and avoiding old ones. Here is a practical guide for this new environment.

Critical Mistakes to Avoid

• Publishing thin content. MUM easily detects a mismatch between a title and its substance.
• Relying only on text. Neglecting multimedia means you will be invisible in many search features.
• Ignoring global competition. Your competitors now publish in any language.
• Disregarding user intent. Failing to understand the user’s full journey will lead to poor results.
• Using structured data incorrectly. Sloppy schema markup can harm rankings instead of helping.

Essential Best Practices to Embrace

• Prioritize high-quality, comprehensive content. Thoroughly answer a user’s query and their next questions.
• Develop topic clusters with multimedia assets. Build authority by covering a subject from all angles.
• Map content to the entire user journey. Create content for every stage, from awareness to decision.
• Implement a robust technical SEO foundation. Use hreflang tags and precise schema markup.
• Build and showcase demonstrable E-A-T signals. Make your expertise and authority clear and verifiable.

Executive Summary: Key Takeaways

The introduction of Google MUM is a pivotal moment for search. Adapting is not optional.

• MUM is a generational leap in AI. It accelerates Google’s shift from a search engine to an answer engine.
• SEO strategy must evolve from keywords to building comprehensive topical authority with multimedia content.
• The competitive landscape is now global. Content competes with the best resources in the world.
• E-A-T (Expertise, Authoritativeness, Trustworthiness) is the universal currency of quality and trust.
• Technical SEO is shifting from ensuring “understandability” to enabling “feature-ability” in rich search results.

Frequently Asked Questions (FAQ)

How is Google MUM fundamentally different from BERT?

MUM is different in several ways. It is about 1,000 times more powerful. It is also built on the T5 framework. This allows it to generate language, not just understand it. Finally, MUM is multimodal and multilingual from the ground up. BERT was primarily a text-based model.

Is Google MUM fully rolled out?

MUM is not a single update. It is a foundational technology. Google is slowly integrating it into its systems. Its influence is already visible in features like “Things to Know” and AI Overviews. Expect this integration to deepen over time.

Will keywords become obsolete because of MUM?

No, keywords will not become obsolete. Their role is changing, however. Keywords are still vital for market research. But your strategy cannot end there. You must now focus on covering the entire topic a keyword represents. The goal is shifting from “ranking for a keyword” to “owning the topic.”

What is the single most important way to optimize content for MUM?

The most important thing is to create the best possible resource on a topic. This means moving beyond text. Use a mix of media to explain concepts clearly. A holistic approach that focuses on the user’s journey is the key to success. Combine this with strong, verifiable signals of E-A-T.