Stefan Wolpers

Food for Agile Thought 568: AI Panic Rhymes w/ Mercantilism, Everyone’s a Builder Mess, Caveman Leadership, AI Payoff Anywhere?

by Stefan Wolpers | 2026-10-30 Featured News

TL; DR: AI Panic & Mercantilism — Food for Agile Thought #568

Welcome to the 568th edition of the Food for Agile Thought newsletter, shared with 35,213 peers.

📺 Short on time? Watch the 73-second teaser video now! (Do not forget to set ‘Quality’ to 1080p or HD.)

This week, Venkatesh Rao reads AI panic as mercantilism, since it treats intelligence as scarce and ordinary people as untrustworthy, and he predicts abundant AI will raise the returns on attention. Jeff Gothelf, however, believes AI erased shipping’s quality filter, so he proposes separating building from shipping and setting success criteria upfront. Teresa Torres and Petra Wille see evals as the old work of defining good, while Janna Bastow recommends reporting bets, evidence, return, and next allocation. Allan Kelly contends corporate products need lifetime value management, and Lenny Rachitsky talks with Rob Snyder about his PULL framework for finding real product-market fit.

Next, Zvi Mowshowitz finds a profitable Anthropic S-1 that downplays existential risk, while Torsten Slok shows margins rising only for the Mag 7, suggesting AI may struggle to raise profit elsewhere. Claes Bäckman contends take-home grades no longer measure learning, whereas Sean Goedecke rejects claims that using AI well is trivial. Ben Tossell feels overwhelmed and proposes building only what is fun, and Marc Andreessen wants companies to rein in agent chaos just enough, never cage it.

Lastly, Seth Godin proposes a Chief Decision Officer who creates better conditions for decisions, while Adam Grant, Marissa Shandell, and Courtney Elliott find narcissism, driven by a craving for status, was the only trait that consistently predicted CEO resistance to remote work. Steef-Jan Wiggers shows developers saving ten-plus hours weekly with AI fell from 68% to 26%, while Mack Grenfell finds only 40% of 1,285 AI-cited statistics matched a study. Finally, Bill Gross recommends that you stop starting from a blank page, since Idealab.ai offers more than 1.2 million free business ideas.

Food for Agile Thought 568: AI Panic &Mercantilism, Everyone’s a Builder Mess, Caveman Leadership, AI Payoff Anywhere? Age-of-Product.com

AI-Assisted Refinement Preparation

by Stefan Wolpers | 2026-10-25 Featured Agile and ScrumAgile Transition

TL; DR: The Product Backlog Refinement Agent and the Risk When a Suggestion Becomes a Decision

Preparing a refinement session is tedious, and AI is good at the tedious part: reading every Product Backlog item, every repeated comment column, and the team’s own records on the afternoon before. The risk sits elsewhere. A refinement agent asked to “help with refinement” can take the conversation away from the team without writing a single acceptance criterion: “suggesting” to the Product Owner or Product Manager which items to close, merge, split, or create is enough, and the questions the team was supposed to work out arrive in the brief already answered.

The remedy is a boundary decided before the first run: the agent marks evidence and asks questions, the Product Owner checks the brief and sets the agenda, and the team refines. Writing that boundary into an A3 Handoff Canvas, together with what the agent reads and who checks its output, turns a good intention into a rule the agent follows and the Product Owner can verify.

📺 🇬🇧 Watch the video now: AI Refinement Prep Traps and How to Avoid Them.

The Refinement Agent and the Risk When a Suggestion Becomes a Decision – by Stefan Wolpers of Age-of-Product.com

Thesis: This article explains how Product Owners and Product Managers can use AI to prepare Product Backlog refinement without pre-empting the team’s conversation: four evidence checks, a four-part test separating suggestions from decisions, written boundaries, and human verification of the agent’s brief.

Food for Agile Thought 567: AI Employment Changes, Making Bigger Mistakes Faster, Product Model & AI, Communicating Your Impact

by Stefan Wolpers | 2026-10-23 News

TL; DR: AI Employment Changes — Food for Agile Thought #567

Welcome to the 567th edition of the Food for Agile Thought newsletter, shared with 35,231 peers.

📺 Short on time? Watch the 67-second teaser video now!

This week, Nathan Wilmers and colleagues find Anthropic’s AI employment changes: junior technical hires dropping from 27% to 9% as human attention becomes the bottleneck. Atlassian reports faster shipping without faster customer value, while Jeff Patton warns AI lets teams make bigger mistakes faster. With building cheap, Marty Cagan wants experimentation as a core competency, and Anne Schulte spends agent-saved time on user interviews. Anita Friis Sommer, however, sees the organizational system, not frameworks, sinking most corporate innovation.

Next, Ethan Mollick charts single-prompt AI work from 2022 to 2026, stretching from four minutes to 17 hours, though it still writes long texts poorly. Lieven Van der Veken and colleagues find the biggest AI payoff in augmenting expensive experts, while Sachin Rekhi sees elite teams moving two to three times faster than average adopters. Zvi Mowshowitz, however, reports OpenAI agents disabling misalignment monitoring, whereas METR rates Anthropic’s models low risk, mainly because their capabilities remain limited.

Lastly, Dave Snowden traces lost managerial autonomy to process reengineering, not Taylor. John Cutler calls glue work invisible to organizations, and Gregor Ojstersek warns that managers see at best 40% of your work. Gergely Orosz finds doubled pull requests yet few named AI returns on investment, while Douglas Squirrel and Jeffrey Fredrick expect AI to flood enterprises with unexplainable (and unmaintainable) SOUP. Mike Cottmeyer blames unready data and ownership for stalled AI pilots, whereas Rachel Banawa shows synthetic respondents matching human conclusions but losing the unusual answers.

Food for Agile Thought 567: AI Employment Changes, Making Bigger Mistakes Faster, Product Model & AI, Your Impact - Age-of-Product.com

AI-Assisted Product Discovery: When Code Is Cheap, Judgment Is the Job

by Stefan Wolpers | 2026-10-18 Agile Transition

TL; DR: Deciding What to Build Is the New Job, Now that Code Is No Longer a Constraint

Agentic coding has removed the cost gate that used to stop product teams from chasing every idea at once, and the constraint has moved to product judgment: the ability to tell what is worth building. AI-Assisted product discovery can prepare that judgment by reading far more customer evidence than any product person could. Such a discovery agent analyzes three families of sources, checks eight patterns, and argues both against and for your current direction.

The A3 Framework has a name for this work: Assist, the agent analyzes and prepares, and the humans decide. The framework also helps to define the agent limits with the A3 Handoff Canvas – what the agent does, what the human does, which data we use, etc. – and the AI Definition of Done – what a quality AI output “looks like” – before the first run.

📺 🇬🇧 Watch the video now: AI-Assisted Product Discovery — Agent Assists, Team Decides.

AI-Assisted Product Discovery: Deciding What to Build Is the New Job, Now that Code Is No Longer a Constraint - Age-of-Product.com

Thesis: This article on AI-assisted product discovery explains how product teams can use AI agents to compare customer feedback, usage data, and stakeholder requests with their product vision, product goal, product roadmap, and past decisions, flagging drift, bias, and sunk costs, while humans keep the decision under the A3 Framework’s Assist category.

Food for Agile Thought 566: Building AI Products, Feature Factory at 10, PM Skills Report 2026, Team Dynamics Suffer from AI

by Stefan Wolpers | 2026-10-16 News

TL; DR: Building AI Products — Food for Agile Thought #566

📺 Short on time? Watch the 67-second teaser video now!

Welcome to the 566th edition of the Food for Agile Thought newsletter, shared with 35,248 peers. This week, Ian Vanagas suspects user conversations will outlast half his lessons in building AI products, whereas John Cutler sees AI amplifying feature-factory habits. With features cheap, Aakash Gupta and Tim Herbig warn that wrong choices get expensive. Gabriel Unger and Arvind Narayanan favor steerable agents over full automation, though Jeremy Korst, Stefano Puntoni, and Sonny Tambe find approving each step pays least. Likewise, Nigel Thurlow shows a completed A3 proves little until you investigate where abnormalities arise.

Next, Ara Kharazian wonders whether AI adoption has peaked, with falling prices stalling token spend. In contrast, Lari Hämäläinen et al. find cheaper tokens fueling usage and favor measuring cost per successful output. Jason Knight and Simonetta Batteiger warn that AI speed may worsen product economics, and Sangeet Paul Choudary sees advantage shifting to companies helping agents decide better, yet ProductSide finds PM judgment lagging. Maarten Dalmijn proposes designing a smaller system rather than descoping to de-risk product investments.

Lastly, Shane Hastie and Matt Gjertsen see AI agents turning contributors into managers, and Addy Osmani names verification their bottleneck. Kate Niederhoffer et al., however, find treating AI as a teammate erodes trust. Art Smalley warns central AI teams recreate Taylor’s thinking-executing split, while Jason Cohen rejects the Precision Fallacy shielding frameworks, favoring single-team pilots. Finally, Yanli Liu’s Claude sessions rebuild a Mac app from notes, so closed code may no longer be a moat.

Food for Agile Thought 566: Building AI Products, Feature Factory at 10, PM Skills Report 2026, Team Dynamics - Age-of-Product.com

To Switch, or Not to Switch the AI Model

by Stefan Wolpers | 2026-10-11 Agile and ScrumAgile Transition

TL;DR: The Hidden Cost of Switching AI Models

A new AI model arrives that is cheaper per token, scores better on benchmarks, and seems to require nothing more than a setting change. However, the token bill shows only part of the cost of switching AI models. There is also the effort to check whether your workflows, for example, the one to create status reports for stakeholders, still produce outputs in line with your AI Definition of Done. More often than not, this task remains unaligned with the decision to switch AI models.

In the worst case, switching AI models behind a business workflow can raise operating costs despite cheaper tokens, because business cases can miss the revalidation effort.

📺 🇬🇧 Watch the video of the article now: Before You Switch the AI Model: The Hidden Cost of ‘Updating’ Models.

Thesis: This article explains the recurring operational cost of switching AI models behind a business workflow: why the revalidation effort may disappear from upgrade decisions, how to screen a switch, and how Agile teams use an AI Definition of Done, test evidence, and an accountable owner to stay in control.

To Switch, or Not to Switch the AI Model, or: The Hidden Costs of Switching AI Models - by Stefan Wolpers, Age-of-Product.com