Almost Timely News: 🗞️ How I Proved Listicles May Harm Your GEO (2026-06-21) :: View in Browser The Big Plug👉 My new course, GEO 201 on competitive GEO measurement, is now for sale. Content Authenticity Statement75% of this week’s newsletter was made by me, the human. You’re going to see a lot of Claude outputs as part of the testing. Learn why this kind of disclosure is a good idea and might be required for anyone doing business in any capacity with the EU in the near future. Watch This Newsletter On YouTube 📺Click here for the video 📺 version of this newsletter on YouTube » Click here for an MP3 audio 🎧 only version » What’s On My Mind: How I Proved Listicles May Harm Your GEOThis week, let’s talk about some articles that have been going around about the impact of listicles on GEO/SEO. If you are unfamiliar, the term listical means list article. That is a long article that is nothing more than a large list, popularized by places like BuzzFeed and adopted by all kinds of companies as a type of content marketing. The one that caught my eye first was by SEO expert Lily Ray, who penned an excellent piece that started everything, that her observations empirically were that listicles were helping competitors more than the companies who published them. She found in her research that alistical made competitors more prominent in citations and results. 69% of the time. I thought that interesting, and wrote up my own LinkedIn post on the topic, which I’ll reproduce here:
I published this, and Matt Trifiro commented:
Matt was correct in that this post was based on my knowledge of generative AI and how LLMs work, and I didn’t present any evidence. So I commented back:
And that’s how we arrived at this week’s issue. Part 1: Designing the ExperimentOne of the great dangers of generative AI is that it’s incredibly easy for us to introduce our biases to it. If I had said to a tool like Claude Code, “prove that I’m right”, it would engineer a sophisticated, compelling, persuasive and intellectually dishonest take. It would design tests and charts and graphs that all looked amazing, and it would use all the symbols of authority to persuade you that I was right. If we value ego, that’s the approach to take, to be incurious, to tell AI to be our cheerleader, to make us look good. If we value truth, that’s the approach to avoid. In this case, I value truth. I genuinely want to know whether listicles help or hurt GEO/SEO. I started by laying out to Claude that I wanted to build a test setup which would use Google’s Gemma 4 model, mainly for efficiency and no cost, to construct listicles and test my hypothesis, that LLMs would, in aggregate, reinforce the competitor more than us once everyone started making listicles. We came up with 3 different kinds of tests. The first is BM25, which is a classical machine learning algorithm that search engines used in the old days to compare different texts and see which was the most relevant. The second is a knowledge graph using the old PageRank centrality measure. The third is an LLM’s recommendations. Part 2: Anticipating BiasesOne of the things generative AI has built into it are biases of literally every kind. Every name, every noun, every topic has some pre-existing connotation based on the training data set. In an experiment like this, where we want to measure the impact of the listicle itself, we have to take those biases out. Lily herself flagged this in her article - LLMs like Google’s Gemini may have biases towards known and trusted brands. If we want to test our hypothesis that the listicle itself, the architecture of information around them, is what’s at play, we have to work with completely fictional, synthetic brands that have no name recognition of any kind. To do this, I relied on the inherent biases of the English language and had Claude choose company names from non-Western word sources - Japanese, Korean, Indonesian/Malay, Swahili, Sanskrit/Hindi, Tagalog, Arabic, Zulu/Xhosa - and then measured whether Gemma 4 had any pre-existing associations. |