TL; DR: Dangerous Agile Myths โ 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 Mรฅrtensson 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 Schoolโs AI ban to Harvardโs 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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๐ The Tip of the Week
: Dangerous Myths and Misconceptions about Agile Software Development
Henrik Mรฅrtensson 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.
๐ฏ Product
and : ๐๏ธ 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.
๐ง 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 โย 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.
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Learn more: ๐ฅ ๐ฏ ๐ฌ๐ง AI4Agile BootCamp #8, August 27 โ September 17, 2026.
Customer Voice: โLast week, I finished the ๐๐ ๐ณ๐ผ๐ฟ ๐๐ด๐ถ๐น๐ฒ ๐ฃ๐ฟ๐ฎ๐ฐ๐๐ถ๐๐ถ๐ผ๐ป๐ฒ๐ฟ๐ course. And Iโm mutatingโฆ 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 couldnโt resist. I tapped: โ๐๐ช๐จ๐ฏ ๐ถ๐ฑโ. What followed were four bi-weekly sessions. Four intense afternoons. Full of exploration, experimentation, and practice. [โฆ] At the beginning, Stefan said that ๐ซ๐ถ๐ด๐ต ๐ด๐ช๐จ๐ฏ๐ช๐ฏ๐จ ๐ถ๐ฑ ๐ข๐ญ๐ณ๐ฆ๐ข๐ฅ๐บ ๐ฑ๐ถ๐ต๐ด ๐ถ๐ด ๐ข๐ฉ๐ฆ๐ข๐ฅ ๐ฐ๐ง ๐ฎ๐ข๐ฏ๐บ ๐ฑ๐ณ๐ข๐ค๐ต๐ช๐ต๐ช๐ฐ๐ฏ๐ฆ๐ณ๐ด. That sounded like a big statement. But somewhere along the way, I noticed a shiftโฆ an emerging superpower in how I approach my tasks with AI.โกAnd now, as my AI-mutation continues, I catch myself wondering: ๐ญ ๐๐ฐ๐ธ ๐ฅ๐ฐ ๐ ๐ถ๐ด๐ฆ ๐๐ ๐ต๐ฐ ๐ด๐ข๐ท๐ฆ ๐ต๐ฉ๐ฆ ๐ข๐จ๐ช๐ญ๐ฆ ๐ธ๐ฐ๐ณ๐ญ๐ฅ?โ (Ilya Zaytsev, Leading Agility at HUGO BOSS.)
โฟ 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.
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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.
๐ 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 Harvardโs 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.
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๐บ 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 โ Hack #01, AI4Agile Online Course v2.
- Socratic Prompting โ Hack #10, AI4Agile Online Course v2.
- Check Your AIโs Plan Before โ Hack #7, AI4Agile Online Course v2.
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๐๏ธ Last Weekโs Food for Agile Thought Edition
The post Food for Agile Thought 553: Dangerous Agile Myths, Produce Evidence Quality, Running Experiments, Playing Politics? appeared first on Age-of-Product.com.
