Stefan Wolpers

Food for Agile Thought #511: AI Bubble, Perfect Product Roadmap, Inversion as Mental Model, Scaling Culture?

by Stefan Wolpers | 2025-09-19 News

TL; DR: AI Bubble — Food for Agile Thought #511

Welcome to the 511th edition of the Food for Agile Thought newsletter, shared with 40,483 peers. This week, Cedric Chin outlines the Vaughn Tan Rule: keep human judgment central while using AI for synthesis, retrieval, transformation, and speed across grading, feedback, research, coding, scheduling, and discovery. Janna Bastow reframes roadmaps as living prototypes tied to strategy and impact. Itamar Gilad urges AI-enabled, evidence-guided discovery over artifact output. Azeem Azhar, with Nathan Warren, proposes five gauges for assessing the AI bubble risk, while Alex Heath interviews Bret Taylor on agentic apps, voice, and outcome-based models.

Next, Teresa Torres and Petra Wille show how real AI products emerge from prompt decomposition, orchestration, observability, and rigorous evals with cross-functional tradeoffs. Jing Hu highlights MIT’s AI Risk Repository and urges post-deployment focus and concrete failure modes. Mike Fisher explains scaling culture through codified values and rituals, and Kent Beck frames programming deflation and the scarcity of judgment. Also, Gergely Orosz and Laura Tacho share how 18 firms measure AI’s engineering impact.

Lastly, Maarten Dalmijn urges context over dogma by adapting or breaking Scrum rules when outcomes suffer. Paul Boag promotes functional, task-driven personas via lightweight AI workflows, and Emma Webster argues AI accelerates speed but not craft, calling for curiosity, intuition, taste, and intention. Also, Shane Hastie interviews Thanos Diacakis on attacking one bottleneck, limiting WIP, and investing 20 to 30 percent in improvement. Finally, Aaron Chatterji and coauthors chart ChatGPT’s global, rising nonwork adoption and decision-support value.

Food for Agile Thought #511: AI Bubble, Perfect Product Roadmap, Inversion as Mental Model, Scaling Culture? Age-of-Product.com

Join the AI for Agile Practitioners Survey — Why We Need Your Insights

by Stefan Wolpers | 2025-09-13 Agile and Scrum

TL; DR: The Agile Community Needs Your Reality Check on AI

After analyzing dozens of “AI will transform agile” articles, I’ve found a troubling pattern: They’re written by AI enthusiasts who’ve never run a Sprint, not by practitioners dealing with the messy reality of AI integration.

The result? A dangerous gap between AI hype and Agile’s reality on the ground, leading to misguided implementations across our industry.

As someone who has documented Agile anti-patterns for years, I recognize this pattern. When we let others define our practices without practitioner input, we get cargo cult implementations that miss the essence of what makes Agile work.

👉 The AI for Agile Practitioners Survey is our opportunity to establish the definitive, practitioner-driven understanding of AI’s actual impact on our field—before the consultants and tool vendors do it for us.

AI for Agile Practitioners Survey: Join the poll and illuminate how artificial intelligence augments Agile - Age-of-Product.com

Food for Agile Thought #510: AI Riches, The Shipping Illusion, Middle-Aged PMs, Enterprise Change Pattern

by Stefan Wolpers | 2025-09-12 News

TL; DR: AI Riches — Food for Agile Thought #510

Welcome to the 510th edition of the Food for Agile Thought newsletter, shared with 40,508 peers. This week, Jerry Neumann analyzes AI Riches, contrasting generative AI with containerization, predicting it will create widespread value but little new wealth for startups or investors. Martin Eriksson shows how product leaders can turn vague growth targets into actionable strategies by mapping opportunities and validating assumptions, and Stephanie Leue challenges the “Shipping Illusion,” advocating for outcome-driven teams that prioritize impact over busyness. Zvi Mowshowitz highlights how rapid AI progress is underestimated and urges preparation for imminent AGI, while Horace He unpacks why LLM reproducibility issues arise and how batch-invariant kernels offer a fix.

Next, Andrew Chen highlights why strong early retention, category fit, timing, and differentiation are crucial for new tech products, as poor retention is nearly impossible to fix later. Steve Newman raises concerns about looming AI agent security risks reminiscent of the early Windows era, and Andi Roberts reframes influence as a daily, relational practice, advocating varied approaches. Simon Powers proposes experiment-driven, people-led change over rigid frameworks, and Roman Pichler clarifies the interplay between strategy, OKRs, and KPIs.

Lastly, Jeff Gothelf urges mid-career product managers to prioritize humility and continuous learning as AI transforms their roles, emphasizing the importance of hands-on AI skills. Ash Maurya explains why billions in AI startup funding vanished, blaming tech without paying customers, ChatGPT competition, and needless complexity, while Joost Minnaar outlines the human skills essential for self-management in flat organizations. Finally, Maarten Dalmijn highlights the hidden costs of high work in progress versus the discomfort of true focus.

Food for Agile Thought #510: AI Riches, The Shipping Illusion, Middle-Aged PMs, Enterprise Change Pattern — Age-of-Product.com

The AI FOMO Paradox: Why Agile Practitioners Are Perfectly Positioned for the AI Era

by Stefan Wolpers | 2025-09-07 Agile and ScrumAgile Transition

TL; DR: AI FOMO — A Paradox

AI FOMO comes from seeing everyone’s polished AI achievements while you see all your own experiments, failures, and confusion.

The constant drumbeat of AI breakthroughs triggers legitimate anxiety for Scrum Masters, Product Owners, Business Analysts, and Product Managers: “Am I falling behind? Will my role be diminished?”

But here’s the truth: You are not late. Most teams are still in their early stages and uneven. There are no “AI experts” in agile yet—only pioneers and experimenters treating AI as a drafting partner that accelerates exploration while they keep judgment, ethics, and accountability.

Disclaimer: I used a Deep Research report by Gemini 2.5 Pro to research sources for this article.

The AI FOMO Paradox: Why Knowledgeable Agile Practitioners Are Perfectly Positioned for the AI Era — Age-of-Product.com

Food for Agile Thought #509: Vibe Coding for Product Managers, Mass Intelligence, Walk Away or Dance, Disruption by GPU

by Stefan Wolpers | 2025-09-05 News

TL; DR: Vibe Coding for PMs — Food for Agile Thought #509

Welcome to the 509th edition of the Food for Agile Thought newsletter, shared with 40,501 peers. This week, Aatir Abdul Rauf highlights the rise of vibe coding for PMs as a core skill, offering 23 tips from prototyping to tool chaining. Arbaz Surti reflects on GPT-5’s turbulent launch, urging PMs to prioritize empathy, transparency, and careful rollouts to preserve trust, and Audrey Xu Leung stresses that experimentation succeeds when rooted in ethical, data-driven cultures of curiosity. Jing Hu explores the AI Enthusiasm Paradox between novices and experts, while Ethan Mollick examines Mass Intelligence reshaping trust, expertise, and work.

Next, Ian Vanagas offers nine lessons for building AI features, from guardrails to continuous evaluation. Teresa Torres shares how simple evals and tracing improved Product Talk’s Interview Coach and reinforced discovery habits. Steve Newman cautions that, despite GPT-5’s progress, agentic AI remains far off. Also, Seth Godin suggests creatives either walk away with slower, deeper work or dance with AI tools, and John Cutler maps nine organizational design patterns and strategies to navigate them.

Lastly, Ron Jeffries warns that overreliance on LLMs erodes learning and true ownership of solutions. Pim de Morree shares how Liip’s pay transparency and Hypoport’s role clarity shape authentic self-management; Brian Balfour highlights sudden product market fit collapses triggered by AI shifts, leaving incumbents scrambling, and Addy Osmani distinguishes vibe coding from disciplined engineering, stressing reviews and tests for quality. Finally, Jenn Spykerman cautions that most AI pilots never scale, urging leaders to measure real production ROI.

Food for Agile Thought #509: Vibe Coding for Product Managers, Mass Intelligence, Walk Away or Dance, Disruption by GPU — Age-of-Product.com

The Generative AI Precision Anti-Pattern: Stop Using LLMs for Problems That Demand Correct Answers

by Stefan Wolpers | 2025-08-31 Agile and Scrum

TL;DR: The Generative AI Precision Anti-Pattern

Here’s another one for your collection: The Generative AI Precision Anti-Pattern, where organizations wield LLMs like precision instruments when they’re probabilistic tools by design. Sound familiar? It’s the same pattern we see when teams cargo-cult agile practices without understanding their purpose.

LLMs excel at text summarization and pattern recognition in large datasets, which helps analyze user feedback or generate documentation drafts, but can they be used for deterministic tasks like calculations? If you are not careful with matching your problem to the right tool, you end up building issues of all kinds into the foundation of your product.

What can you do about it? Spoiler alert: The fix isn’t better prompting, but architectural discipline and tool-job alignment.

The Generative AI Precision Anti-Pattern: Stop Using LLMs for Problems That Demand Correct Answers — Age-of-Product.com