You can choose one AI company for the rest of your days. You may use any tool it creates—and any technology from companies it acquires. What are you choosing? No switching. No backup company. No “it depends.” Which AI ecosystem are you betting on for the rest of your days—and why?

Since I started using Claude Cowork more regularly, I noticed something strange: My creative writing seemed to suffer. The ideas were still there. The information was accurate. The structure was often better. But the writing felt more formal, organized, and buttoned up. It did not always sound like me. So I asked Claude Cowork: “Why does my creative writing seem to suffer in Cowork compared with Chat?” The answer helped me realize that I was not necessarily using the wrong AI. I was using the wrong environment for that stage of the work. Chat is Dory. 🐠 It follows ideas wherever they go. It: explores, wanders, changes direction, and occasionally stumbles into something unexpectedly good. That makes Chat especially useful for brainstorming, finding your voice, testing language, and developing an idea before you know exactly where it will end. Cowork is Spock. 🖖 It wants the facts, the files, the instructions, and the desired outcome. It is, more structured, logical, organized, and focused on completing the assignment. In other words: Chat is often better for finding the voice. Cowork is better for turning that voice into finished work. Neither one is better. They are simply better at different stages of the process. My current approach is simple: - Use Chat to discover what I want to say. - Use Cowork to organize, refine, research, and complete the work. The mistake was expecting Spock to think like Dory. In Part 2, I will look at why Chat and Cowork feel so different from a feature and product-design perspective.

As AI models continue to evolve, they are beginning to give us different products for different kinds of use cases. The three products I find most useful are: - Chat - Work - Code There are also specialized products for things such as design, research, and working with source material—including tools like Claude Design, Gemini Notebook. But for today, let’s stick with the three horsemen: Chat, Work, and Code. Each AI company is known for its models. The large, well-known models—ChatGPT, Claude, and Gemini—are commonly referred to as frontier models and are generally associated with closed, cloud-based systems rather than open-source models running locally. Please refer to the "Frontier AI Ecosystem Matrix" to see what OpenAI, Anthropic, Google, Microsoft, xAI, and Perplexity call their respective models and products. For this series, I will use the follow product naming convention: - Chat - (Co)Work - Code(x) The parentheses are simply my shorthand to cover the names used by ChatGPT and Claude. Please note that these are not official industry terms. They simply save me from repeatedly writing “Work or Cowork” and “Code or Codex.” LOL! Here is the easiest way I have found to explain the difference between the products: Chat: What question can I answer for you, right now? (Co)Work: What can I take off your plate? Code(x): What can I build for you? The goal is not to create a perfect technical definition. It is to give you a simple way to recognize which type of AI product may be the best fit for the task in front of you. In Part 2, we will look more closely at Chat, (Co)Work, and Code(x) product features, how each product is actually used and where the lines between products overlap. In Part 3, we will work through a few practice exercises to help solidify the use cases for each product. If you have any questions, please ask in the comments below. Make it a great AI day!

This is a follow-up to my original “Wine Glass Test” — a simple experiment that turned into something more interesting. After my first post, I received a thoughtful suggestion from @Matthew Sutherland. His advice was straightforward: Be more prescriptive. So I refined the prompt to this: “Create a glass of wine that is full, red wine. It needs to be at the brim, so not to run over, and not below the brim to show any space between the brim and the surface of the wine in the glass.” The image below is the direct result. And the result is telling. 🍷 What This Actually Proves This wasn’t about aesthetics. It was about bias and instruction. When I originally asked for a “full glass of wine,” the model produced what most restaurants would call full — but still left space at the top. That’s not an error. That’s statistical bias. The model leaned into the most common interpretation of “full.” When the instruction became extreme and structured, the behavior changed. It complied precisely. 🍷 There are two observations that I see with this test: 1️⃣ Prompting Is a Skill We often talk about model bias as if it’s a flaw. It’s not. It’s probability doing what probability does. My first prompt allowed the model to default to “standard pour.” The refined prompt removed ambiguity. By defining the boundary conditions — no gap, no overflow — the model had to break from its average tendency and execute exactly. That’s not luck. That’s instruction design. Prompting isn’t just writing a sentence. It’s mapping expectation into structure. And as Matthew pointed out, that skill develops iteratively. 2️⃣ Natural Language Still Has Friction The deeper takeaway isn’t that the model can create a perfectly full glass. It’s that everyday language is still ambiguous to it. When a human says “full glass of wine,” we infer intent through context. The model infers through probability. Those are not the same. For AI to feel seamless in daily life, we shouldn’t need to mathematically define “full.”

In this video, we walk through a simple but powerful introduction to NotebookLM — Google’s AI tool for organizing, understanding, and working with your information. Using a realistic example, we take customer service meeting minutes and bring them into NotebookLM to see what it can do. You’ll see how quickly it can: - Summarize source material - Answer questions based only on your documents - Generate process flow infographics - Create mind maps to visualize logic and structure - Help validate whether AI actually understands your workflow This isn’t about perfect outputs — it’s about learning how to use AI as a thinking partner. If you’re just getting started, try this: Take any meeting notes, drop them into NotebookLM and explore the tools. It’s one of the fastest ways to move from “AI curious” to "AI Enthusiast" by actually trying it and applying it. We’ll go deeper into more advanced features in upcoming videos. 💬 Questions? Drop them in the comments