Learn to Think in Color

How Are Network Effects Impacted by Generative AI? When your flywheel meets a stochastic parrot with superpowers Big-picture framing Generative AI is reshaping  network effects and generative AI dynamics  at every layer: data, users, and products. In classic platforms, more users mean more value for each user; now, more users also mean more data, faster learning, and better models. The core question shifts from “How big is our network?” to “How quickly can we compound learning from our network?” Instead of replacing network effects, AI  bends and intensifies  them. It can turn weak flywheels into strong ones, transform single-sided products into ecosystems, and in some cases even erode old moats by lowering switching costs. If you build, invest in, or compete with AI products, understanding these new loops is now part of the job. Network effects 101 (in one minute) Classic  network effects : The product gets more valuable as more people use it. Each new user add...

How Can You Use Consequences in Decision Making? Clarifying choices by thinking through the ripples, not just the splash. Big-picture framing Using  consequences in decision making  means looking beyond “What do I want?” and asking, “Then what… and then what?” Instead of judging options only by how they feel right now, you deliberately forecast the short-term and long-term outcomes each path is likely to create. This shift turns vague pros and cons into clearer stories about the future: who’s affected, what changes, and what risks you’re really accepting. By slowing down to imagine different consequence pathways—including ethical ones—you make more grounded, less reactive choices that are easier to defend to yourself and others later. Why consequences are your hidden dataset Every decision is a bet on the future, and consequences are the data you’re (often unconsciously) using to place that bet. Most people do this intuitively: you  feel  that one option is safer or ...

How Do You Ensure Your Data Will Solve Your Business Problem? Aligning Information With Intention: From Raw Data to Real Decisions Before diving into solutions, take a beat. The biggest mistake teams make isn’t bad data—it’s disconnected data. Ensuring your data solves your business problem requires asking better questions, aligning stakeholders, and translating business objectives into data models that actually drive decisions. This post walks through how to frame the right problem, structure your data thinking, test its effectiveness, and—critically—know when data won’t help at all. Step 0: Know When Data Won’t Help Not every problem needs more data. Before launching a data project, ask: Does this require quantitative analysis or strategic judgment? Leadership alignment issues, brand positioning, and creative direction often need qualitative insight, not dashboards. If stakeholders won’t act on results due to politics or if the analysis costs more than the problem’s worth, stop here....