🐹 Hamsterdam Part 132: Weekly SEO & AI News Recap (1/10 to 1/16, 2026)
By Ethan Lazuk
Last updated:
A weekly look-back at SEO & AI news, tips, and other content shared on social media & beyond.
Opening notes, thoughts, and musings:
- Welcome to another new week of Hamsterdam! 🐹
- I appreciate you being here, and look forward to sharing the week’s news! 🙌
If you’re in a rush, hop down to the news portion. 📰
Or continue reading for two vocabulary lessons plus an introduction.
And we’re off …
🧑💻 Marketing word of the week: “Knowledge-based marketing”
Knowledge-based marketing is just as it sounds: rather than relying on gut instinct to guide marketing decisions, your strategy uses data and research. In short, it’s marketing driven by what a company knows about customers, markets, and performance.
Some of the goals of knowledge-based marketing include using customer data, analytics, as well as accumulated experience to understand customer need and behavior, predict demand and preferences, design more relevant campaigns, and improve long-term marketing strategies.
Rather than asking “what do we think will work?,” knowledge-based marketing asks “what does the evidence tell us?”
Core components of knowledge-based marketing include customer knowledge (like demographics, purchase history, online behavior), market knowledge (like competitors, industry trends, pricing norms), and organizational knowledge (like past campaign results, internal best practices, brand positioning lessons).
In practice, knowledge-based marketing might be used for segmentation, targeting, personalization, forecasting, or general optimization.
🤖 AI word of the week: “Knowledge inference”
This is a core concept in AI that explains how a system derives new information from what it already knows. In short, knowledge inference is how an AI reasons. In detail, knowledge inference is the process by which an AI system draws logical conclusions from existing knowledge using rules, logic, and probabilistic reasoning. The system isn’t given new data, but rather it’s inferring new facts from stored information.
A typical inference process involves a knowledge base (stores facts, rules, and relationships, for example, “all mammals are warm-blooded”), an inference engine (applies reasoning mechanisms to the knowledge base, for example, “a dog is a mammal”), and a derived conclusion (new knowledge produced through reasoning, for example, “a dog is warm-blooded”).
There are different types of knowledge inference.
Deductive inference moves from general rules to specific conclusions, for example “all humans are mortal, Socrates is human, therefore Socrates is mortal.”
Inductive inference draws general conclusions from specific examples, for instance after observing many spam emails the system learns spam patterns.
Abductive inference infers the most likely explanation for an observation, for example a system failure is most likely caused by a power loss.
Probabilistic inference handles uncertainty using probabilities, for example, given symptoms it can predict the probability of a disease.
Knowledge inference is used in a variety of cases, including expert systems (medical diagnosis or legal reasoning), knowledge graphs (entity reasoning and link prediction), natural language understanding, decision-supported systems, and recommendation systems.
😊 Introduction to week 132: “Chunking Debate”
Chunking content was all the rage, and the justifications for it made sense, based on how AI models digest content.
Then Danny Sullivan said Google doesn’t prefer it.
Personally, I’ve always stood on the side of great content for SEO or GEO is still great content for users, and much of the optimization strategies around content for AI visibility, like clear headings, BLUF paragraphs, bullet points, FAQs, etc., comes down to great writing and structure for users.
I’m still a fan of chunking content in semantically rich and distinct sections because I believe that equates to good writing.
Where I’m against chunking is when it’s done arbitrarily or just for the sake of AI visibility, excluding considerations for readability.
If it makes sense for your readers, I say chunk away. And if it doesn’t, then go another route. It might just be as simple as that.
Thank you for supporting Hamsterdam and the cause of SEO & AI learning. 🙏
Enjoy the vibes:
Missed last week? Don’t worry, I got you! Read Part 131 to catch up.
🌟 Other great sources of weekly SEO news:
- The SEO Weekly – Garret Sussman, iPullRank
- SEOFOMO – Aleyda Solis
- Weekly Video Recaps – Barry Schwartz, SER
- Weekly SEO News YouTube channel – Olga Zarr, Seosly
- Niche Surfer – Yoyao Hsueh
Time for our weekly review of SEO social posts, articles, & more …
Now, let’s step inside the white flags of Hamsterdam …
⏩ Jump to a section:
- News, Google updates, & SERP tests
- SEO tips & tidbits
- Fundamentals & resources
- Articles, videos & case studies
- Local SEO
- Technical SEO
- Content marketing
- Local SEO
- Data analysis & reporting
- AI, LLMS, & machine learning
- Miscellaneous & general posts
- Older stuff that’s good!
Or keep scrolling to see it all. ⏬
📰 SEO news, Google updates, SERP tests & notable posts
Notable updates or news related to Google Search or related SEO topics.
🍟 SEO tips & tidbits
Actionable tips, cool tidbits, and other snackable findings and observations that can be teaching moments.
Essential information, concepts, or resources to learn about SEO or AI
Longer-form content pieces shared on social, in newsletters, and elsewhere.
🧑💻 Technical SEO
Everything from basics to advanced moves (and also tools).
✍️ Content marketing
From what is helpful content to user journeys and beyond.
📍 Local SEO
From Google Business Profiles to reviews and more!
📊 Data analysis & reporting
Showing that what you’re doing is helping.
🤖 AI, machine learning, & LLMs
News related to models, papers, and companies.
🤔 General marketing & miscellaneous
This is for great content that isn’t necessarily SEO or marketing-specific. PPC, PR, dev, design, and social friends, check it out!
💎 Older stuff that’s good!
Not everything I find worth sharing is new as of this week, so these are gems I came across published in the past.
Let’s connect!
Hit me up anytime via text or call at 813-557-9745 or on social or email:
Cheers! ✌️
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