Fable comes back online today. We've all heard the name for weeks, so here's a quick guide on what it is and (more importantly) when to actually use it, so we don't waste money. First things first: What is Fable? (for anyone new to it). Let's imagine Anthropic's AIs like a "family of brains". The regulars are Opus, Sonnet and Haiku. Fable is the new, smartest one, the first of a new generation (the Mythos family), able to do things no other model could. Basically, it's the strongest Claude we can get. It disappeared for 3 weeks: - June 9: Fable launches, everyone's talking about it. - June 12: Amazon researchers find a trick that makes Fable point out software weaknesses (and once even write code to break in). The U.S. government blocks it, and since Anthropic can't check everyone's nationality fast enough, they switch it off for everybody. - Late June: they negotiate a deal with the government. - July 1 (today): Fable comes back on, worldwide. (By the way: other AIs like Opus 4.8, GPT-5.5 and China's Kimi K2.7 could all do the same thing that the Amazon researchers found. So it wasn't some unique "super weapon". Just a simple jailbreak that worked in other models, and those other models were powerful enough to point out those weaknesses too.) Now, the important part of this...WHEN do we use Fable instead of Opus? Is it really that powerful? Let's use an analogy. Think of a race. Opus is a great sprinter. Fable is a marathon runner that checks its own work as it goes. Fable's edge is long, hard jobs that run on their own. Anthropic's own words: "the longer and more complex the task, the larger Fable's lead." So the best option to use Fable is when the task: - takes hours or days, not minutes - has many steps and we don't want to babysit, supervise or check each one - needs to hold a LOT at once (a big codebase, a pile of documents... up to 1,000,000 tokens) - should check its own work and keep going until it's done - is genuinely hard (senior-level reasoning, a big migration, deep research)

AI doesn’t sit still. So how do you design for a moving target? Terminology changes. Tools rotate. Models leapfrog each other. What sounds cutting-edge today can feel narrow tomorrow. We’ve already watched the cycle: “ChatGPT Consultant.” “Prompt Engineering Agency.” “GPT Automation Studio.” Each made sense at the time. Each anchored to a moment. That’s the risk. 🎯 Tool Anchoring Is a Short Shelf-Life Strategy When you anchor your company name to: - A specific LLM - A specific interface - A specific tactic - A specific trend You’re betting that layer remains dominant. In AI, that’s rarely a safe bet. The model landscape shifts. Capabilities expand. Language evolves. Your name shouldn’t expire with the cycle. 🎯 Design for Expansion, Not Just Accuracy Future-proof names are designed for range. They allow you to move from: Prompting → Automation Automation → Agents Agents → Orchestration Orchestration → Strategy Without renaming the company. They also age better in enterprise settings. A CFO is less interested in today’s tool. They’re interested in operational durability. Your name should signal that. 🎯 The Five-Year Test Before locking in a name, ask: Will this still make sense in five years? Will this still sound credible in a boardroom? Will this limit the services I can offer? If the answer creates hesitation, reconsider. Because rebranding later isn’t cosmetic. It resets key legal and marketing facets that have real implications: - Trademark filings and legal protections - Domain authority and SEO history - Backlinks and search equity - Brand recognition in the market - Client references and case studies - Contracts, agreements, and documentation - Marketing collateral and positioning assets It’s not just a new logo. It’s administrative work. It’s marketing disruption It’s strategic distraction. That’s why future-proofing isn’t about sounding timeless. It’s about reducing unnecessary friction five years from now. 🎯 The Strategic Principle

A Weekly Smash Up is a recap lesson where we connect the last five AI Terms into one big idea — showing how they work together in practice. Think of it as a highlight reel for your AI learning. This week is all about the building blocks of AI — what it is, the models behind it, and how we start talking to it. For this, we smash up Terms 1–5 of the AI Terms Beginner Classroom course. Here’s how they connect! 🟦 AI (Term 1) Artificial Intelligence is the umbrella — teaching computers to act smart.🔧 In practice: Think of AI as the “smart helper” built into your tech. 🟩 Large Language Model (LLM) (Term 2) A type of AI trained on massive amounts of text to understand and generate language.🔧 In practice: The LLM is the engine that powers the smart helper. 🟨 GPT (Term 3) Generative Pre-trained Transformer — the specific type of LLM powering ChatGPT.🔧 In practice: GPT is the engine design — specialized for predicting and generating words. 🟧 ChatGPT (Term 4) The chatbot application built on top of GPT.🔧 In practice: ChatGPT is the car you drive, powered by the GPT engine. 🟥 Prompt (Term 5) The instruction you give to ChatGPT to get an answer.🔧 In practice: Your prompt is the steering wheel — guiding where the car goes. 🔄 The Flow in Action AI → the big field LLM → the type of model GPT → the specific engine ChatGPT → the tool you use Prompt → the way you drive it That’s the foundation of AI in plain English — the what, the how, the tool, and the way you interact with it.