Link Archive

TL;DR — Stop freestyle prompting Claude Code. Three-phase system: Spec (define marketing outcome, launch milestones, create project spec with marketing and technical requirements), Stack (seven-step config including claude.md with brand guidelines, tracking standards, integration patterns, MCPs for analytics/CRM/deployment), Ship (three workflows: general for single pages, campaign-based for multi-asset launches, multi-agent for parallel development). Key insight: 15 minutes of speccing saves weekend debugging. Always verify tracking before considering anything done. 'Page is live' is not done. 'Conversions recording correctly' is done.

TL;DR — OpenAI co-founder admits feeling behind despite building these systems. Marketing parallel is direct: the gap between marketers using AI as a feature and marketers orchestrating AI workflows is widening fast. His vocabulary (agents, prompts, contexts, memory, tools, plugins, workflows) maps to marketing ops. The 10X productivity claim requires stringing tools together correctly. Key insight: failure to capture AI leverage is now a skill issue, not access issue. Same tools available to everyone. Competitive advantage shifts to those who build mental models for 'stochastic, fallible' systems. Marketers face identical challenge: learn to orchestrate unreliable-but-powerful AI across content, analytics, automation.

TL;DR — LLMs use the same probability system to explain themselves as they do to answer questions. When you ask 'why did you recommend that?', you get another statistical lottery, not truth. SparkToro tested 100 people asking ChatGPT identical knife recommendation prompts. Almost no two got the same brand list. When asked to explain, ChatGPT fabricated reasoning. Marketers making decisions based on LLM self-explanations are building on false foundations. The only honest answer: 'most likely token based on training data.' Applies directly to anyone using AI for brand tracking, competitor analysis, or content recommendations.

TL;DR — Brand web mentions correlate at 0.664 with AI visibility vs. 0.218 for backlinks. Top 25% brands earn 10x more AI Overview citations. AI search visitors convert at 4.4x traditional organic. 26% of brands have zero AI Overview mentions. Seven tactics: earned media, expert commentary, podcasts, conferences, content partnerships, analyst relations, community participation.

TL;DR — AI Overviews reduce clicks to top-ranking pages by 34.5% (Ahrefs). 60% of searches end without a click (Bain). CTR drops from 15% to 8% when AI Overviews present (Pew). 75% of AI Mode sessions end without external visits. Stop measuring by traffic volume. Track AI citation share across ChatGPT, Gemini, Perplexity.

TL;DR — Average domain age of ChatGPT-cited sources is 17 years. Schema markup moved from nice-to-have to mandatory. AI systems favor entity recognition over keywords and backlinks. Roy's Entity Authority Engineering framework: consistent structured data, mapped citations, multi-model testing.

TL;DR — Early-stage moats are 69% internal (team expertise, product velocity) and 31% external (workflows, data, integrations). Growth-stage inverts to 64% external, 36% internal. Late-stage is 96% external ecosystem. AI makes expertise and features easier to copy. Ecosystems take years to build and are extremely sticky.

TL;DR — Cloudflare and Conductor data shows AI platforms drive only 1% of web traffic across 10 major industries. ChatGPT accounts for 87.4% of all AI referral traffic. Perplexity has the worst crawl-to-refer ratio at 700:1.

TL;DR — Google launched the =AI() function in Sheets. Type a prompt in any cell to generate text, summarize data, or categorize rows at scale. Supports multi-step tasks like deleting archived rows, inserting columns with dropdowns, and bulk find-and-replace.