TL;DR: A Harvard Study of Procter & Gamble Shows the Way
Recent research shows AI isn’t just another tool—it’s a “cybernetic teammate” that enhances agile work. A Harvard Business School study of 776 professionals found individuals using AI matched the performance of human teams, broke down expertise silos, and experienced more positive emotions during work. For agile practitioners, the choice isn’t between humans or AI but between being AI-augmented or falling behind those who are. The cost of experimentation is low; the potential career advantage, on the other hand, is substantial. A reason to embrace generative AI in Agile?
TL; DR: Reshaping Teamwork — Food for Agile Thought #486
Welcome to the 486th edition of the Food for Agile Thought newsletter, shared with 42,656 peers. This week, Fabrizio Dell’Acqua et al. reveal how AI can match team performance and boost collaboration in a Procter & Gamble field study. Julie Zhuo dismantles the traditional product development playbook, while Karri Saarinen champions craft and quality over speed. Itamar Gilad calls for transformational AI visions, not just incremental ones. And Paul Roetzer warns AGI may be closer than we think—raising the stakes for how we lead, build, and stay human.
Next, Zvi Mowshowitz critiques Sam Altman’s casual AGI stance, contrasting it with disruption-heavy models like Epoch’s GATE. Aakash Gupta and Tal Raviv demonstrate building an AI-powered product management copilot in under an hour. McKinsey explores how large organizations are restructuring to unlock gen AI value. Plus, Tobias Mayer and Jade Garratt explore Scrum, safety, and leadership at work.
Lastly, Matheus Lima argues that actual psychological safety thrives on respectful conflict, not artificial harmony. Ian Vanagas identifies communication pitfalls engineers face, while Jeff Gothelf exposes the hidden costs of misused OKRs. Finally, Petra Wille shares why AI notetakers don’t belong in coaching—some moments are too human for machines to capture without consequence.
Vibe coding — using natural language to generate code through AI — represents a significant evolution in software development. It accelerates feedback cycles and democratizes programming but raises concerns about maintainability, security, and technical debt.
Learn why success likely requires a balanced approach: using vibe coding for rapid prototyping while maintaining rigorous standards for production code, with developers evolving from writers to architects and reviewers or auditors.
TL; DR: Agile Coach Bubble Origins — Food for Agile Thought #485
Welcome to the 485th edition of the Food for Agile Thought newsletter, shared with 42,652 peers. This week, Viktor Cessan explores the Agile Coach bubble origin, which was inflated by cheap capital. Marty Cagan and Felipe Castro stress that outcomes over outputs remain elusive without adopting the product model. Maarten Dalmijn identifies systemic dysfunction—not poor PMs—as the real challenge and proposes CACAO as a remedy. David Pereira urges teams to treat the backlog as a strategic tool, not a dumping ground. Also, Lenny Rachitsky interviews Anton Osika on how Lovable hit $10M ARR in 60 days by letting users describe apps in plain language—AI handles the rest.
Next, Ben Thompson interviews Sam Altman on OpenAI’s evolution from research lab to consumer tech giant, unpacking ChatGPT, AGI, and pursuing “the next Facebook.” Klaudia Jaźwińska and Aisvarya Chandrasekar expose how AI search engines misattribute and fabricate news content, damaging trust, and publisher economics. Peter Yang offers 12 grounded rules for building real apps with AI—especially for non-coders, while Charity Majors dismantles the 10x engineer myth, advocating for resilient, inclusive teams where “normal” engineers thrive. And Andy Cleff makes a case for self-selection: empowered teams deliver better outcomes than top-down assignments ever could.
Lastly, Rita McGrath defends bureaucracy’s stabilizing role but calls for permissionless systems. Ben Kuhn shares his Anthropic crisis project management lessons, and John Cutler uses LLMs to visualize individual interactions, which are documented during interviews. Finally, Ted Neward reframes organizational “debt” as strategic or destructive—urging awareness.
TL; DR: Bridging Agile and AI with Proper Prompt Engineering
Agile teams have always sought ways to work smarter without compromising their principles. Many have begun experimenting with new technologies, frameworks, or practices to enhance their way of working. Still, they often struggle to get relevant, actionable results that address their specific challenges. Regarding generative AI, there is a better way for agile practitioners than reinventing the wheel team by team—the Agile Prompt Engineering Framework.
Learn why it solves the challenge: a structured approach to prompting AI models designed specifically for agile practitioners who want to leverage this technology as a powerful ally in their journey.
Welcome to the 484th edition of the Food for Agile Thought newsletter, shared with 42,673 peers. This week, Ethan Mollick examines vibe coding, where AI-native teams blend human expertise and AI to iterate and collaborate rapidly. Maarten Dalmijn critiques rigid planning, advocating for elastic teams that thrive in complexity. In anticipating engineering’s shift toward AI management roles, Jasper Gilley quit his FAANG job, seeing automation redefine technical careers. Michael Küsters likens middle management to Rock-Paper-Scissors, where unpredictability is key to success. At the same time, Fred Hebert dissects AI integration, emphasizing thoughtful human-in-the-loop design to avoid automation pitfalls.
Next, Itamar Gilad argues that product success is rare due to underestimated complexity and misaligned forces, advocating for strategic clarity and intentional culture-building. Then, David Pereira highlights how pilot testing helps PMs validate assumptions, reduce risks, and iterate faster, and Brian Balfour predicts AI will redefine product teams—transforming methodologies, roles, monetization, and distribution—urging AI-native strategies. In an interview with Peter Yang, Anthropic’s Scott White details how Claude 3.7 Sonnet accelerates product development through AI-generated PRDs, evals, and agentic coding tools.
Lastly, we critique overly blameless post-mortems, advocating for balanced accountability to prevent mediocrity, and Johanna Rothman champions rolling-wave planning to reduce pressure and improve adaptability. Ash Maurya stresses that successful pivots require rapid business model testing and external accountability. Additionally, Zvi Mowshowitz dissects Manus, a Chinese AI agent hyped as groundbreaking but revealed as a Claude wrapper, and, finally, Andrew Chen explores “vibe coding,” where AI reshapes software creation, predicting a shift toward intuitive, GUI-driven design, fragmented UX, and adaptive, self-improving applications.
