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Food for Agile Thought #513: No AI-Disruption, Building Influence as a PM, 2025 Product Metrics, Against Generative AI

by Stefan Wolpers | 2025-10-03 News

TL; DR: No AI-Disruption — Food for Agile Thought #513

Welcome to the 513th edition of the Food for Agile Thought newsletter, shared with 40,441 peers. This week, Martha Gimbel, Molly Kinder, Joshua Kendall, and Maddie Lee report that there have been no economy-wide AI-disruption of the labor market since 2022 and call for better usage data. Maarten Dalmijn warns AI sped shipping bloats products and urges subtraction-minded PMs. Brian Balfour and Lauryn Motamedi rethink SaaS pricing by leveraging system-level levers and providing customer education. Also, Ethan Mollick shows near-expert AI agents shifting tasks under expert oversight, and Naval Ravikant advocates for iterative simplification and clear ownership.

Next, Chad McAllister interviews Rich Mironov on product leadership that speaks revenue, merchandises wins, cuts waste, and mentors for pragmatic team design. At the same time, Jana Paulech cautions against endless discovery and advocates simple, hypothesis-led research tied to business goals. Edward Zitron argues the generative AI boom is a fragile, Nvidia-dependent bubble. Leah Tharin spotlights 2025 benchmarks where activation speed drives retention, and OpenAI unveils GDPval, expert-graded tasks showing frontier models nearing expert quality.

Lastly, Jing Hu reports research showing that AIs favor AI-written content by 60 to 95 percent, urging audits of AI gatekeepers and strategic polishing without compromising human judgment. Seth Godin frames AI as infrastructure, shifting value to ambition, taste, and community. John Cutler, on the other hand, warns against comforting narratives, urging leaders to co-author cause-and-effect stories and surface risks early. Finally, Barry O’Reilly rejects maturity models, favoring outcome metrics, experiments, coaching, and DORA-like measures.

Food for Agile Thought #513: No AI-Disruption, Building Influence as a PM, 2025 Product Metrics, Against Generative AI — Age-of-Product.com

AI Risks: Why Product Professionals Are Sleepwalking Into Strategic Irrelevance

by Stefan Wolpers | 2025-09-28 Agile and ScrumAgile Transition

TL; DR: AI Risks — It’s A Trap!

AI is tremendously helpful in the hands of a skilled operator. It can accelerate research, generate insights, and support better decision-making. But here’s what the AI evangelists won’t tell you: it can be equally damaging when fundamental AI risks are ignored.

The main risk is a gradual transfer of product strategy from business leaders to technical systems—often without anyone deciding this should happen. Teams add “AI” and often report more output, not more learning. That pattern is consistent with long-standing human-factors findings: under time pressure, people over-trust automated cues and under-practice independent verification, which proves especially dangerous when the automation is probabilistic rather than deterministic (Parasuraman & Riley, 1997; see all sources listed below). That’s not a model failure first; it’s a system and decision-making failure that AI accelerates.

The article is an extension to the lessons on “AI Risks” of the Agile 4 Agile Online course; see below. The research of sources was supported by Gemini 2.5 Pro.

The AI Risks Trap: Why Product Professionals Are Sleepwalking Into Strategic Irrelevance — Age-of-Product.com

Food for Agile Thought #512: DORA 2025, Roadmap Illusion, Context Rot, Does Anyone Care About Product?

by Stefan Wolpers | 2025-09-26 News

TL; DR: DORA 2025 State of AI-Assisted Software Development — Food for Agile Thought #512

Welcome to the 512th edition of the Food for Agile Thought newsletter, shared with 40,467 peers. This week, Google Cloud’s DORA team debuts its DORA 2025 State of AI-Assisted Software Development, featuring seven capabilities, seven team profiles, and a case for value stream management. Rich Mironov urges product leaders to tell money stories, protect infrastructure, and translate bets for executives, and Stephanie Leue exposes roadmap fragility and argues for balanced allocations. Jing Hu critiques consumer-skewed AI usage and vanity adoption metrics, while John Cutler reframes the trade-offs between vertical and horizontal coupling.

Next, Teresa Torres and Petra Wille demystify AI evals from golden datasets to guardrails, Jenny Wanger argues strategy filters beat scoring frameworks, and Zvi Mowshowitz unpacks Nvidia’s rumored OpenAI stake, 10GW buildout, and Stargate implications. Kelly Hong, in conversation with Hamel Husain, explains the concept of context rot and urges the practice of disciplined context engineering. Also, Shane Hastie interviews Shannon Mason on why 100 percent utilization harms teams, advocating intentional slack, fewer context switches, and capacity planning aligned with strategy.

Lastly, James Reggio and Camilla Matias demonstrate how Brex’s AI platform automates step-by-step workflows, improving onboarding and communication, while IDEO defines people-first leadership through six learnable skills. Andy Cleff ties the Yes Habit to five thieves of time and advocates visible WIP and saying no. Moreover, Michael Bellato, Mårten Schultzberg, and Sebastian Ankargren share Spotify’s Experiments with Learning metric. Finally, Kevin Kelly proposes a periodic table of cognition, forecasting the emergence of many distinct AI minds.

Food for Agile Thought #512: DORA 2025, Roadmap Illusion, Context Rot, Does Anyone Care About Product? — Age-of-Product.com

AI Transformation Déjà Vu: Why Today’s Failures Look Uncannily Like Yesterday’s “Agile Transformations”

by Stefan Wolpers | 2025-09-21 Agile and ScrumAgile Transition

TL;DR: AI Transformation Failures

Organizations seem to fail their AI transformation using the same patterns that killed their Agile transformations: Performing demos instead of solving problems, buying tools before identifying needs, celebrating pilots that can’t scale, and measuring activity instead of outcomes.

These aren’t technology failures; they are organizational patterns of performing change instead of actually changing. Your advantage isn’t AI expertise; it’s pattern recognition from surviving Agile. Use it to spot theater, demand real problems before tools, insist on integration from day one, and measure actual value delivered.

AI Transformation Failure Déjà Vu: Why Today’s Failures Look Uncannily Like Yesterday’s “Agile Transformations” — Age-of-Product.com

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