Food for Agile Thought 550: Make AI Boring, Everyone’s a Product Manager Soon, Fixing Procrastination, Agentic “Team” Topologies

TL; DR: Make AI Boring — Food for Agile Thought #550

Welcome to the 550th edition of the Food for Agile Thought newsletter, shared with 35,481 peers.

This week, Charity Majors rejects AI purity theater and urges disciplined workplace experiments, just make AI boring again, while Gojko Adzic warns that faster builders without product judgment will ship polished waste. Dave Hora names the organizational traps that keep teams from seeing reality, and Johanna Rothman brings the fix down to flow data and human judgment. Azeem Azhar and colleagues see AI demand rising, but Satya Nadella argues that a durable advantage comes from owning learning itself.

Next, Paweł Huryn moves AI work from prompt craft to agent loops with goals, guardrails, budgets, and independent checks, while Jeff Gothelf argues that AI pilots fail when firms bolt tools onto stale workflows. Joe Hudson adds that emotional clarity now beats knowledge hoarding, and John Cutler names fear, incentives, and executive fantasies as the real bottlenecks. David Burkus brings the pattern back to procrastination, where stress and ambiguity demand clarity without control.

Lastly, Elena Verna pushes experimentation beyond tiny UI tweaks toward larger monetization bets and longer engagement signals, as Zvi Mowshowitz warns AI policy needs calibrated safeguards rather than theater. Deborah Rim Moiso brings the same discipline to facilitation through communities that review real work, and Olivier Wulveryck applies Team Topologies to agentic platforms before shadow IT hardens. Finally, Itamar Gilad grounds the pattern in value, not misleading productivity counts.

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🏆 The Tip of the Week

Charity Majors: Make AI Boring Again

Charity Majors believes AI is neither special nor pure evil, but rather powerful technology with real harms. Opting out may feel clean, but it abandons the field. Tech workers should learn, govern, and constrain AI through boring, disciplined workplace experiments, shared norms, accountability, and repair, rather than purity theater or posturing.

Source: Make AI Boring Again

Author: Charity Majors

🎯 Product

Gojko Adzic: In five years, everyone will be a product manager

Gojko Adzic suggests AI will shrink teams and spread product management work everywhere. Without customer empathy, bets, prioritization, and discovery skills, faster builders will ship better-engineered junk at scale.

Source: In five years, everyone will be a product manager

Author: Gojko Adzic

Dave Hora: The Four Horsemen: Archetypal problem patterns in product orgs

Dave Hora proposes four product-org traps: self-sealing narratives, stagnant motion, clinging to past success, and defensive operations. The fix starts with shared attention, reality checks, and small tests for change.

Source: The Four Horsemen: Archetypal problem patterns in product orgs

Author: Dave Hora

Jeff Gothelf: How product management can fix your AI integration problems

Jeff Gothelf believes AI pilots fail when companies bolt tools onto old workflows. Product management can give teams authority to redesign work, measure outcomes, and scale evidence carefully.

Source: How product management can fix your AI integration problems

Author: Jeff Gothelf

Pawel Huryn: Agent Loops for PMs: 20+ You Can Run This Week

Paweł Huryn suggests PMs stop worshipping prompts and start defining agent loops with clear goals, guardrails, budgets, and independent checks, because fuzzy definitions of done quietly turn automation into expensive slop fast.

Source: Agent Loops for PMs: 20+ You Can Run This Week

Author: Pawel Huryn

Elena Verna: The AI era requires a different kind of experimentation.

Elena Verna believes AI makes old experimentation playbooks obsolete: stop wasting engineering time on tiny UI tweaks, test bigger monetization bets, measure engagement over months, and skip validation for obvious basics.

Source: The AI era requires a different kind of experimentation.

Author: Elena Verna

🧠 Artificial Intelligence

📖 Azeem Azhar et al: The state of the AI economy

Azeem Azhar and colleagues suggest AI demand is already real: revenues are growing fast, infrastructure costs may be covered, and falling token prices could expand usage rather than shrink spend.

Source: 📖 The state of the AI economy

Author: Azeem Azhar

Joe Hudson (via Lenny Rachitsky): The new inner game: Your unfair advantage in the age of AI

Joe Hudson suggests AI makes emotional clarity the new career moat: discernment, conflict tolerance, failure appetite, and better self-talk now matter more than knowledge hoarding or performative busyness in teams.

Source: The new inner game: Your unfair advantage in the age of AI

Authors: Lenny Rachitsky and Joe Hudson

Satya Nadella: A frontier without an ecosystem is not stable

Satya Nadella proposes that companies must own their learning, not rent it from frontier models. Durable AI advantage comes from human capital, token capital, digital sovereignty, and systems that compound expertise.

Source: A frontier without an ecosystem is not stable

Author: Satya Nadella

Zvi Mowshowitz: The Once And Future Fable #4

Zvi Mowshowitz proposes that blocking Fable 5 was messy theater: public access may return, but development keeps racing, cyber risks are real, and sane AI policy needs calibrated safeguards, not panic or wishful thinking.

Source: The Once And Future Fable #4

Author: Zvi Mowshowitz

🖥 💯 🇬🇧 AI4Agile BootCamp #8, August 27 — September 17, 2026

The job market’s shifting. Agile roles are under pressure. AI tools are everywhere. But here’s the truth: the Agile professionals who learn how to work with AI, not against it, will be the ones leading the next wave of high-impact teams. Therefore, Stefan created the AI4Agile BootCamp.

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Learn more: 🖥 💯 🇬🇧 AI4Agile BootCamp #8, August 27 — September 17, 2026.

Customer Voice: “Last week, I finished the 𝗔𝗜 𝗳𝗼𝗿 𝗔𝗴𝗶𝗹𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝘁𝗶𝗼𝗻𝗲𝗿𝘀 course. And I’m mutating… It started on the train. I was scrolling through my messages, half-distracted, when a newsletter from Stefan Wolpers popped up. Stefan, a deep thinker with a hands-on attitude, was launching a new course. A pilot cohort. The mission: explore how AI can actually support us as agile practitioners. I couldn’t resist. I tapped: “𝘚𝘪𝘨𝘯 𝘶𝘱”. What followed were four bi-weekly sessions. Four intense afternoons. Full of exploration, experimentation, and practice. […] At the beginning, Stefan said that 𝘫𝘶𝘴𝘵 𝘴𝘪𝘨𝘯𝘪𝘯𝘨 𝘶𝘱 𝘢𝘭𝘳𝘦𝘢𝘥𝘺 𝘱𝘶𝘵𝘴 𝘶𝘴 𝘢𝘩𝘦𝘢𝘥 𝘰𝘧 𝘮𝘢𝘯𝘺 𝘱𝘳𝘢𝘤𝘵𝘪𝘵𝘪𝘰𝘯𝘦𝘳𝘴. That sounded like a big statement. But somewhere along the way, I noticed a shift… an emerging superpower in how I approach my tasks with AI.⚡And now, as my AI-mutation continues, I catch myself wondering: 💭 𝘏𝘰𝘸 𝘥𝘰 𝘐 𝘶𝘴𝘦 𝘈𝘐 𝘵𝘰 𝘴𝘢𝘷𝘦 𝘵𝘩𝘦 𝘢𝘨𝘪𝘭𝘦 𝘸𝘰𝘳𝘭𝘥?” (Ilya Zaytsev, Leading Agility at HUGO BOSS.)

➿ Agile & Leadership

John Cutler: The Bottleneck Strike Again!

John Cutler suggests that engineering is no longer the bottleneck in lazy theater. Product development’s real constraints are shifting conversations, incentives, coordination, fear, and executive fantasies about massive AI-driven headcount cuts.

Source: The Bottleneck Strike Again!

Author: John Cutler

David Burkus: How To Fix Your Team’s Procrastination Problem (Without Becoming A Micromanager)

David Burkus believes team procrastination is rarely laziness. It is usually stress, ambiguity, weak milestones, slow leadership, or fuzzy norms, so managers should create real clarity without suffocating ownership.

Source: How To Fix Your Team’s Procrastination Problem (Without Becoming A Micromanager)

Author: David Burkus

Johanna Rothman: How and When to Use AI to Diagnose & Avoid Common Agility Problems, Part 3

Johanna Rothman suggests LLMs help only when teams feed them real flow data. Use AI to visualize bottlenecks, but keep humans responsible for hard judgment, retrospectives, and messy why questions.

Source: How and When to Use AI to Diagnose & Avoid Common Agility Problems, Part 3

Author: Johanna Rothman

📯 The AI Definition of Done: Human in the Loop Is Not a Quality Standard

Your team has a Definition of Done for a product increment. It has none for the 20-plus AI-supported outputs that leave the team each week: status reports, stakeholder emails, release notes, and updates for the C-level. Each one carries your team’s name.

“I know quality when I see it” is the standard most teams actually run by, and you cannot audit it, teach it to a new colleague, or defend it when a claim turns out to be wrong. The AI Definition of Done fixes that with one page per task class, agreed by the team, before the output ships.

Thesis: The AI Definition of Done is a one-page, team-agreed standard that an AI-assisted output must meet before it leaves the team. You write one per task class, never per task: one for external status communication, one for data analysis summaries, one for backlog item drafts. It borrows the discipline of the Scrum Definition of Done and applies it to work that has been touched, especially outputs that leave the team. This is Stage 4 of the AI Delegation Lifecycle. The sections below cover the four questions it answers and how to write yours in 75 minutes.

Learn more: The AI Definition of Done: Human in the Loop Is Not a Quality Standard.

🛠 Concepts, Practices, Tools & Measuring

Deborah Rim Moiso (via SessionLab): How to build an internal facilitation community of practice

Deborah Rim Moiso suggests facilitation sticks when organizations stop treating training as the solution and build communities where practitioners share methods, review work, learn safely, and standardize while preserving judgment.

Source: SessionLab: How to build an internal facilitation community of practice

Author: Deborah Rim Moiso

Olivier Wulveryck: Who Does What? Team Topologies for the Agentic Platform

Olivier Wulveryck proposes Team Topologies for agentic platforms: business teams drive intent, platform teams absorb technical risk, and enabling teams prevent AI-powered shadow IT from becoming tomorrow’s fastest organizational debt machine.

Source: Who Does What? Team Topologies for the Agentic Platform

Author: Olivier Wulveryck

Itamar Gilad: How To Measure Developer Productivity?

Itamar Gilad suggests that developer productivity metrics such as commits, pull requests, tokens, or story points can mislead teams. Measure value creation instead, using outcome goals for cross-functional teams and organizations, not individuals.

Source: How To Measure Developer Productivity?

Author: Itamar Gilad

📅 Training Classes, Meetups & Events 2026

Upcoming classes and events:

• 🖥 💯 🇬🇧 July 1 — Meetup: HoA 75: Token Economics (English)
• 🖥 💯 🇬🇧 July 20 — Self-Paced Online Course: AI 4 Agile Course v3 — Master AI for Agile Practitioners (English; Self-paced Online Course)
• 🖥 💯 🇬🇧 July 23 — Meetup: HoA 76: Where Are Your Decisions to Avoid AI Debt? (English)
• 🖥 🇩🇪 August 25–16, 2026 — Live Virtual Class: Professional Scrum Product Owner Training (PSPO I; German)
• 🖥 💯 🇬🇧 August 27-September 17 — Live Virtual Cohort: AI4Agile BootCamp #8 (English)

👉 See all upcoming classes here

🗞️ The Previous Food for Agile Thought Edition

Food for Agile Thought 549: AI in Product 2026, Makers Manifesto, AI POM, Open Knowledge Format.

📺 Join 6,000-plus Agile Peers on YouTube

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Food for Agile Thought 550: Make AI Boring, Everyone’s a Product Manager Soon, Fixing Procrastination, Agentic “Team” Topologies was first published on Age-of-Product.com.