Rod Claar traces a through-line from his earliest work in a 1972 lumber yard to his current role as an AI trainer — revealing that the core skill connecting both worlds is pattern recognition. The post reframes AI for skeptics and late adopters: AI isn't magic, it's pattern matching at scale. Professionals with decades of real-world problem-solving experience already possess the foundational thinking that makes AI useful. The message is empowering — your past experience is an asset, not a liability, in the age of AI.

Five facts from the past week — a stronger Claude, metered Copilot billing, a cheap new Grok coding model, a more autonomous Cursor, and a permanent DeepSeek price cut — and what each means for your Scrum team.

Rod Claar draws a direct parallel between writing clean code and writing effective AI prompts. The core idea: vague instructions produce broken results, whether you're coding or prompting. Developers already have the structured thinking required to write great prompts — they just need to apply it to AI. The post offers four practical rules for better prompts and closes with a concrete example showing the difference between a weak prompt and a precise, program-like one.

Of the five Scrum values — Commitment, Focus, Openness, Respect, and Courage — most teams practice four reasonably well. Courage is the one they avoid. Yet Courage is what makes the other four values meaningful. Without it, Openness becomes performative, Commitment turns into silent pressure, and Respect becomes an excuse not to speak up. Courage shows up in small, practical moments: telling the Product Owner the backlog isn't ready, admitting uncertainty during estimation, or raising a real problem in the Daily Scrum. After 20 years working with Scrum teams, the clearest differentiator between teams that improve and teams that stagnate is willingness to say what's true — even when it's uncomfortable. Courage doesn't require a dramatic stand. It just requires speaking up one moment sooner than feels safe.

Over a 50-year career spanning lumber yards, retail software, Scrum and AI-assisted development, one principle has remained constant: the best professionals think clearly about the problem before reaching for a tool. Whether it was a pencil and clipboard in 1972 or AI in 2026, the tool itself was never the differentiator — the quality of thinking behind it was. The same holds true today: great developers define the problem before opening an IDE, write tests before writing code, and ask AI a good question before accepting its answer. Tools will keep changing. Clear thinking never goes out of style.