TL; DR: Dangerous Agile Myths — Food for Agile Thought #553
Welcome to the 553rd edition of the Food for Agile Thought newsletter, shared with 35,462 peers. This week, Henrik Mårtensson dismantles seven dangerous Agile myths, showing that fat-tailed cycle-time data invalidates the use of story points. Teresa Torres and Petra Wille question whether support tickets can replace story-based interviews, while Roman Pichler pushes visions beyond feature lists toward purpose. Turning to AI, Laura Summers finds LLM-assisted coding replaces building satisfaction with supervision fatigue, Benedict Evans sees foundation models becoming commodities, and Satya Nadella urges firms to own their learning loops before providers capture proprietary knowledge.
Next, John Cutler reframes software assets through a portfolio lens, asking whether AI makes you faster or moves you faster in the wrong direction. George Sivulka and Arvind Narayanan both place the bottleneck in management, not model capability. On the human side, Sean Goedecke redefines engineering politics as knowing who holds power and making contributions visible, while Steven Sinofsky compares Chicago Law School’s AI ban to Harvard’s 1982 computer ban, arguing such restrictions never last.
Lastly, Pavel Samsonov argues that product empathy rings hollow without respect, a gap LLMs deepen by pushing error correction onto users. Thomas Squeo and Matt Kamelman trace enterprise AI failure to missing governance, not weak models. Susan MacKenty Brady, Stuart Kliman, and Leslie Smith name four leadership traps quietly eroding trust. Finally, Dave Rooney rethinks story slicing when AI handles large tasks, and Tristan Kromer notes AI accelerates experiments but cannot pick the right question.
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🏆 The Tip of the Week
: Dangerous Myths and Misconceptions about Agile Software Development
Henrik Mårtensson tackles seven persistent agile myths, including 'agile is a mindset,' 'the manifesto contains all you need,' and 'Agile equals Scrum.' Using real project data, they show that estimates and story points fail because software development cycle times follow fat-tailed distributions rather than normal ones. Skills beat slogans.
🎯 Product
and : 🎙️ Quality of Evidence
Teresa Torres and Petra Wille discuss why not all product evidence is equal: low-effort signals, like support tickets, can feel informative but rarely tell teams what to build without story-based interviews.
: How to Create a Truly Inspiring Product Vision
Roman Pichler suggests that product visions fail when they describe features or business goals instead of stating a true purpose, and recommends using emotionally resonant language co-created in collaborative workshops.
: Empathy and delight mean nothing when the software is disrespectful
Pavel Samsonov suggests that empathy and delight in product design ring hollow without respect, and that LLMs amplify this problem by removing user control and shifting the burden of error-checking onto people.
: Incubate, Compound, Refinance, Liquidate
John Cutler proposes a portfolio lens for software assets (incubate, compound, refinance, liquidate) and suggests the real AI question is not whether it makes you faster, but in which direction.
🧠 Artificial Intelligence
(via Pydantic): The Human-in-the-Loop is Tired
Laura Summers proposes that LLM-assisted programming is both useful and destabilizing: it automates the satisfying parts of coding while replacing them with the exhausting cognitive load of supervising mostly-correct output.
: Ways to think about token pricing
Benedict Evans proposes that every visible market dynamic points toward foundation models becoming low-margin commodity infrastructure, and that sustainable pricing power would require something to change we cannot yet see.
(via Andreessen Horowitz): The Next AI Goldrush: Tokens, Loops, and Neofirms — You just hired a million bad employees.
George Sivulka proposes that AI agent workforces fail the same way human ones do: most token spend is wasted on loops, and the real bottleneck is management, not model capability.
: What will be left for us to work on?
Arvind Narayanan proposes that AI is transformative but will not replace workers anytime soon, as real bottlenecks lie in organizational adaptation, reliability gaps, and evaluation, not in model capability alone.
(via ThoughtWorks): The operating system for enterprise AI
Thomas Squeo and Matt Kamelman propose that enterprise AI fails not because of weak models but because organizations lack an 'organizational harness': the governance layer to delegate, control, and learn from agentic work at scale.
🖥 💯 🇬🇧 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
: The Reverse Information Paradox
Satya Nadella warns that enterprises risk leaking proprietary knowledge to AI providers through everyday usage and proposes that firms must control their own learning loops, evals, and model outputs to protect their competitive edge.
(via Harvard Business Review): 4 Hidden Traps of Team Dynamics
Susan MacKenty Brady, Stuart Kliman, and Leslie Smith identify four leadership traps that silently erode trust in diverse teams: certainty, saying one thing while doing another, emotional reactivity, and self-justification.
: What does 'playing politics' mean for software engineers?
Sean Goedecke proposes that 'playing politics' for software engineers is not about scheming but about knowing who holds power, avoiding unnecessary conflicts with them, and making your contributions visible to the right people.
📯 You Already Have an AI Working Agreement. Write It Down.
Your team already has rules for using AI. Some live in templates, some in habits, exceptions, and one person’s memory. The AI Working Agreement puts the decisions that matter in one place: what the team delegates to AI, what stays human, what must be reviewed, what never enters a model, who owns which workflow, and how the agreement changes. Write it, and a new colleague can read your team’s AI decisions on their first day, while the decisions stay when someone leaves.
Thesis: Team-level AI governance fails more from uncodified judgment than from missing policies. The AI Working Agreement turns scattered AI decisions into one inspectable artifact, so a team can onboard people, survive departures, and challenge its own habits before those habits harden into risk.
Learn more: You Already Have an AI Working Agreement. Write It Down.
🛠 Concepts, Practices, Tools & Measuring
: Banning AI in Law School: We've Seen This Before
Steven Sinofsky draws parallels between Chicago Law School's recent ban on AI and Harvard’s 1982 ban on computers, and suggests that preemptive restrictions on transformative tools have never survived contact with reality.
: Rethinking 'Small'
Dave Rooney suggests that AI coding tools change the story-slicing calculus: when inputs and outputs are well known, one large story delivered with LLM help can beat twelve thin slices.
(via Kromatic): Before You Run the Experiment, Pick the Right Question
Tristan Kromer proposes that AI can accelerate the running of experiments, but cannot choose the right question to test. Picking the wrong question remains the single most common reason founders get useless data.
📅 Scrum Training & Event Schedule
You can secure your seat for Scrum training classes, workshops, and meetups directly by following the corresponding link in the table below:
| Date | Class and Language | City | Price |
|---|---|---|---|
| 🖥 💯 🇬🇧 July 20, 2026 | GUARANTEED: AI 4 Agile Course v3 — Master AI for Agile Practitioners (English; Self-paced Online Course) | Self-Paced Online Course | $149 incl. 19% VAT (If applicable.) |
| 🖥 🇩🇪 August 25-26, 2026 | Professional Scrum Product Owner Training (PSPO I; German; Live Virtual Class) | Live Virtual Class | €999 incl. 19% VAT (If applicable.) |
| 🖥 💯 🇬🇧 August 27-September 17, 2026 | GUARANTEED: AI4Agile BootCamp #8, August 27 – September 17, 2026 (English; Live Virtual Cohort) | Live Virtual Cohort | €499 incl. 19% VAT (If applicable.) |
| 🖥 💯 🇬🇧 Sep 28-29, 2026 | GUARANTEED: A3 Delegation System Founding Workshop (English; Live Virtual Class) | Live Virtual Class | $199 incl. 19% VAT (If applicable.) |
| 🖥 🇩🇪 Sep 30-Oct 1, 2026 | Professional Scrum Product Owner Training (PSPO I; German; Live Virtual Class) | Live Virtual Class | €999 incl. 19% VAT (If applicable.) |
See all upcoming classes here.
You can book your seat for the training directly by following the corresponding links to the ticket shop. If the procurement process of your organization requires a different purchasing process, please contact Berlin Product People GmbH directly.
📺 Join 6,000-plus Agile Peers on Youtube
Now available on the Age-of-Product YouTube channel to improve learning, for example, about Dangerous Agile Myths:
- Stop Writing Prompts. Let AI Do It for You — Hack #01, AI4Agile Online Course v2.
- Socratic Prompting — Hack #10, AI4Agile Online Course v2.
- Check Your AI’s Plan Before — Hack #7, AI4Agile Online Course v2.
- From Product Requirements to Experiments to Learnings — Supported by Generative AI.
- Never Accept an LLM’s First Offer — Improve GenAI’s Usefulness w/ Feedback Loops and Challenges.
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Help your team to learn about how AI Intensifies Work by pointing them to the free Scrum Anti-Patterns Guide:
