Every AI conversation starts from zero because the model forgets who you are. The Claude Cowork Online Course teaches you to change that: build persistent Skills, connect your tools, and assemble agents for recurring work. No coding.
Thesis: “A prompt disappears after one use; a Skill compounds across every session.”
Eric Ries’ new book ‘Incorruptible’ solves a problem most readers will not face for years: protecting a valuable organization from capture once it succeeds. The builders that AI is creating hit an earlier issue: Building software used to force the question of whether it was worth building. That gate has largely collapsed. Eric Ries asks how mission survives success; we, the normals, how judgment survives abundance.
Thesis: Ries’s Incorruptible solves a later problem, protecting a valuable organization from capture; cheap building created an earlier one, where judgment about what is worth building is the only gate left. That is the problem this article addresses.
Agentic coding tools have collapsed the friction of producing plausible software; output is no longer an issue. However, they have not collapsed the friction of knowing what is worth building, whether it fits the system, or whether users will change their behavior because of it, the much-desired outcome. When generating plausible code becomes cheap, every hour spent building the wrong thing becomes waste that can now be produced at scale. Discovery, validation, product judgment, and verification are what stand between your team and creating expensive waste at high-speed.
Thesis: AI made generating code cheap enough that weak product judgment can now scale. That is the problem this article addresses.
Twenty years of agile coaching failed to fix the micromanager who meddles with every draft, every meeting, every decision. This article shows where their distrust stops damaging teams and starts producing the verification work AI adoption actually needs. Welcome the Verification Architect!
Two weeks ago, I asked my audience whether they wanted a short course on moving from Scrum to a Product Operating Model, and 22 answered. That was not the Scrum-to-POM dataset I hoped for, but it was valuable for the conversations. Interestingly, one pattern ran through more than a quarter of the responses: The people writing back were not asking about transformation practices or operating models. They were asking what was about to happen to their jobs.
Let me paraphrase some of their replies: One Agile Coach wrote that their role had already been made redundant, and the internal training their employer offered was not enough. Another asked a blunt question: “What will happen to my role?” A third described leadership, saying they wanted this shift, while their behavior remained inconsistent. A fourth reported confusion about what a product coach actually is. A fifth dismissed the whole discourse as high-level fluff, transformational buzzwords, zero accountability, and vague systems thinking with no teeth.
My takeaway: While the organizational design debate appears to be the surface, the ongoing role repositioning is what the people on the ground are living through.
AI tools are reshaping how Scrum Teams work, and Scrum Masters who cannot coach their teams through this shift are not ready for 2026. This article presents ten Scrum Master interview questions that test whether a candidate can facilitate AI adoption without losing self-management. As usual, each question includes guidance on answers and red flags. The questions are drawn from the seventh edition of the 97 Scrum Master Interview Questions guide.
