2026: The Year of the LLM Listicle

Quick Overview

The speaker predicts that 2026 will be the "Year of the LLM Listicle," where Large Language Models (LLMs) like ChatGPT will heavily reference listicles, even those they are not explicitly cited by, as a core part of their topical authority and ranking strategy for SEO, urging viewers to listicle everything they can access, including client, business, and parent sites.

Key Points: The speaker predicts 2026 will be the "Year of the LLM Listicle" due to how Large Language Models (LLMs) prioritize listicles for topical authority. LLMs, especially ChatGPT, reference a ton of DR0 sites when generating content, suggesting Domain Rating (DR) may not matter as much for AI output. Topical authority might be back (or 'back up') for ChatGPT, as the AI consistently references niche sites. The speaker tested this by querying for 'best physio in east vancouver,' noting that a client's site, Movement Room, was not ranked #1 initially but became #1 after running LLM PBN tests. The core strategy suggested is to 'Listicle all sites you have access to, even if not cited it helps,' including client sites, business sites, parent sites, and Reddit posts. The speaker's daily revenue tracking showed $10,939.825 total, with $177.8 from Amazon, $153.95 from Medevine, and $719.25 from Adult sources. The speaker plans to build an AI writer for LLMs to outrank competitors, similar to how they tested PBNs, suggesting this aggressive listicle strategy is highly effective now.

Context: The speaker, Jacky Chou (wearing a LocalRank hat), hosts a regular update or discussion, seemingly on SEO or business strategy, referencing daily revenue figures and experiments conducted with AI tools like ChatGPT. The video focuses on predictions for the future of SEO influenced by LLMs, specifically concerning the value of 'listicles' (articles formatted as lists) in influencing AI-generated search results.

Detailed Analysis

The speaker makes a bold prediction that 2026 will be the "Year of the LLM Listicle," arguing that Large Language Models (LLMs) heavily rely on listicles to establish topical authority, even when those listicles do not directly cite the source. He notes that in his testing, LLMs reference a ton of DR0 sites, implying that traditional Domain Rating (DR) might become less critical for AI-driven rankings. He presents a case study where, after running LLM PBN (Private Blog Network) tests, a client's site ranked #1 for a competitive local query ('best physio in east vancouver') where it previously ranked lower. The key takeaway is an aggressive strategy: listicle everything—clients' sites, business sites, parent sites, and Reddit posts—because if the content is accessible to the LLM, it helps build authority. He mentions his recent revenue was $10,939.825, broken down by source, and suggests that building an AI writer that can outrank competitors using this listicle strategy will be crucial for future success.