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Hands-on Agile by Stefan Wolpers · Jul 19, 2026

Food for Agile Thought 553: Dangerous Agile Myths, Produce Evidence Quality, Running Experiments, Playing Politics?

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Stefan Wolpers · Hands-on Agile by Stefan Wolpers

Hello everyone!

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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Did you miss the previous Food for Agile Thought issue 552?

🎓 Join Stefan in one of his upcoming training classes!

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.

Source: Dangerous Myths and Misconceptions about Agile Software Development

Author: Henrik Mårtensson

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.

Source: 🎙️ Quality of Evidence

Authors: Teresa Torres and Petra Wille

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.

Source: How to Create a Truly Inspiring Product Vision

Author: Roman Pichler

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.

Source: Empathy and delight mean nothing when the software is disrespectful

Author: Pavel A. Samsonov

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.

Source: Incubate, Compound, Refinance, Liquidate

Author: John Cutler

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.

Source: Pydantic: The Human-in-the-Loop is Tired

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.

Source: Ways to think about token pricing

Author: Benedict Evans

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.

Source: Andreessen Horowitz: The Next AI Goldrush: Tokens, Loops, and Neofirms — You just hired a million bad employees.

Author: George Sivulka

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.

Source: What will be left for us to work on?

Author: Arvind Narayanan

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.

Source: ThoughtWorks: The operating system for enterprise AI

Thanks for reading Hands-on Agile by Stefan Wolpers! This post is public so feel free to share it.

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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.)

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.

Source: The Reverse Information Paradox

Author: Satya Nadella

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.

Source: Harvard Business Review: 4 Hidden Traps of Team Dynamics

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.

Source: What does ‘playing politics’ mean for software engineers?

Author: Sean Goedecke

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.

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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.

Source: Banning AI in Law School: We’ve Seen This Before

Author: Steven Sinofsky

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.

Source: Rethinking ‘Small’

Author: Dave Rooney

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.

Source: Kromatic: Before You Run the Experiment, Pick the Right Question

Author: Tristan Kromer

Upcoming classes and events:

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Food for Agile Thought 552: AI Creates Jobs? Product Roadmaps & Leader Anxiety, Overthinkers, Measuring ≠ Learning.

Now available on the Age-of-Product YouTube channel to improve learning, for example, about AI’s Labor Market Impact:

Also:

Read the original on handsonagile.substack.com ↗

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