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Food for Agile Thought 570: Tech Job Market 2026, AI Product Management ROI, Bureaucracy’s Intrinsic Motivation, Where Is AI’s VisiCalc?

by Stefan Wolpers | 2026-11-13 Featured News

TL; DR: Tech Job Market 2026 — Food for Agile Thought #570

Welcome to the 570th edition of the Food for Agile Thought newsletter, shared with 35,177 peers.

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

This week, Lenny Rachitsky finds PM openings near four-year highs and “LLM” topping job descriptions, yet Henrik (Henok) Lindström et al. see no single AI PM role. With dedicated research possibly vanishing, Teresa Torres and Petra Wille test continuous discovery, Torres wondering whether it must adapt. Rich Mironov doubts 10x coding speed means 10x revenue, while Richard Kasperowski’s panel finds profitability the hard part, and Tugce Erten warns cheaper models strengthen customers’ build-it-yourself option.

Next, Tim Tully et al. find AI spend up 5.4x, and Ara Kharazian shows 1% of businesses driving 80% of OpenAI and Anthropic revenue. Yet Cristina Catania et al. urge CFOs to redesign one finance process, not fund isolated AI pilots. While Tim O’Reilly compared AI to the “Internet of 1997,” Ethan Mollick’s almost-AGI Fable 5.2 experience still needed his judgment. Also, Alan Sharp sees product creation bottlenecks moving to judgment, such as requirements and review, so Agile’s values matter now more than ever.

Lastly, Stacy Gordon and Len Greski show AI token bills getting paid with headcount because spend is visible and value is not, while David Burkus suggests cutting junior roles created employers’ own talent shortage. Mike Fisher documents the opposite reflex: departments preserving problems that justify their headcount. With 43% of incident.io’s PRs merging without human review, as Gergely Orosz reports, Marc Abraham shows an LLM judge can score 90% accuracy and yet catch zero errors.

Food for Agile Thought 570: Tech Job Market 2026, AI Product ROI, Bureaucracy’s Motivation, Where Is AI’s VisiCalc? - Age-of-Product.com

We’re Not Paid to Practice Scrum, but Answer 5 Product Questions

by Stefan Wolpers | 2026-11-08 Featured Agile and ScrumAgile Transition

TL;DR: Cheap Code Makes 5 Product Questions Expensive to Skip

Thesis: This article explains five product questions that become more consequential when AI makes software implementation cheap, why falling AI costs increase the volume of work those questions must filter, and how teams set working principles during early AI adoption.

📺 Watch the article’s video: We’re Not Paid to Practice Scrum — Answer These 5 Product Questions Instead.

For decades, the cost of building software rationed what an organization could start: nobody could afford to build everything, so somebody had to choose, however crudely. AI has weakened that rationing; the frontier labs need your organization to use more AI with every model generation, and the product operating model promises to fix what Agile supposedly broke. None of this guarantees that anyone asks whether the work solves a customer problem, nor whether solving it was worth the cost. You need to address these product questions yourself.

These product questions become more expensive to skip when starting work gets cheap: which problem is worth solving, whether the work is the best use of the team’s time, whether it is good enough for the people who will see it, whether it solved the problem, and whether the team’s way of working still fits. AI agents can prepare evidence under the A3 Framework’s Assist category, where the agent analyzes and people decide, while explicit standards for AI-assisted work, such as the AI Definition of Done, and recurring inspections, such as the AI Delegation Audit, keep the answers honest. Whether you call any of this Scrum is the least interesting question of all.

We're Not Paid to Practice Scrum, but Answer 5 Product Questions: When AI makes software implementation cheap - Age-of-Product.com

From Agile to the Product Operating Model: What Practitioners Say Is Actually Changing

by Stefan Wolpers | 2026-08-10 Featured Agile TransitionLean and Product

TL; DR: The “Agile to the Product Operating Model” Survey Results

Between August 2 and August 10, 2026, 48 practitioners participated in my Agile to Product Operating Model (POM) survey, which tries to shed light on what is actually changing.

Let me summarize the answers for you: the reported transformations change decision-making less than the Cagan framework suggests. Where respondents report improvements, they appear in delivery and collaboration rather than in business results. Unfortunately, the human side of the transition is the least encouraging part of the answers.

Take all the following information with a grain of salt, given that the sample size is so small. (I recall the good times when 1,000 to 2,000 people would participate in a Scrum master salary report, but those times seem to be over, despite my asking 39,000 people for their contributions via a newsletter.)

Agile to the Product Operating Model Survey Results: What Practitioners Say Is Actually Changing – Age-of-Product.com

Thesis: Product operating model transformations mostly change vocabulary and organizational structure while leaving the decision system, who decides what gets built, on what evidence, at what speed, largely untouched. AI is changing product decisions independently of POM transformations.

The AI4Agile Foundational Assessment: A Free Practical Judgment Benchmark for Agile Practitioners

by Stefan Wolpers | 2026-03-28 Featured Agile and ScrumAgile Transition

Using AI at Work Does Not Mean You Understand It

Many agile practitioners use ChatGPT at work. That does not mean they understand AI well enough to trust your own judgment. The problem is not that agile practitioners ignore AI. The problem is that many already use it confidently without knowing where their judgment breaks down. The free AI4Agile Foundational Assessment measures precisely this skill gap. (Download your access file below.)

The assessment comprises 40 scenario-based questions. It does not ask for definitions, but puts you into situations that agile coaches, product managers, and Scrum Masters face every week: weak prompting producing generic output, misleading data analysis, questionable agent output, and, possibly, organizational pressure to treat AI output as “good enough” to go with it.

Most people who use AI do not fail because they lack knowledge, but because they cannot distinguish between plausible outputs and trustworthy judgment. But see for yourself!

AI4Agile Foundational Assessment: A Free Practical Judgment Benchmark for Agile Practitioners - Age-of-Product.com

The A3 Handoff Canvas: Six Questions That Turn AI Delegation Into a Repeatable Workflow

by Stefan Wolpers | 2026-02-15 Featured Agile and ScrumAgile Transition

TL;DR: The A3 Handoff Canvas

The A3 Framework helps you decide whether AI should touch a task (Assist, Automate, Avoid). The A3 Handoff Canvas covers what teams often skip: how to run the handoff without losing quality or accountability. It is a six-part workflow contract for recurring AI use: task splitting, inputs, outputs, validation, failure response, and record-keeping. If you cannot write one part down, that is where errors and excuses will enter.

The Handoff Canvas closes a gap in a useful pattern: from an unstructured prompt to applying the A3 framework to document decisions with the A3 Handoff Canvas, to creating transferable Skills, potentially leading to building agents.

The A3 Handoff Canvas: Six Questions That Turn AI Delegation with the A3 Framework Into a Repeatable Workflow — Age-of-Product.com

The A3 Framework: Assist, Automate, Avoid — A Decision System for AI Delegation

by Stefan Wolpers | 2026-01-11 Featured Agile and ScrumAgile Transition

TL; DR: The A3 Framework

The A3 Framework categorizes AI delegation before you prompt: Assist (AI drafts, you actively review and decide), Automate (AI executes under explicit rules and audit cadences), or Avoid (stays entirely human when failure would damage trust or relationships). Most AI training teaches better prompting. The A3 Framework teaches the prior question: Should you be prompting at all? Categorize first, then prompt.

The A3 Framework: Assist, Automate, Avoid — A Decision System for AI Delegation to Preserve Professionalism — Age-of-Product.com

Agile Is Dead, Long Live Agility

by Stefan Wolpers | 2025-12-07 Featured Agile and ScrumAgile Transition

TL; DR: Why the Brand Failed While the Ideas Won

Your LinkedIn feed is full of it: Agile is dead. They’re right. And, at the same time, they’re entirely wrong.

The word is dead. The brand is almost toxic in many circles; check the usual subreddits. But the principles? They’re spreading faster than ever. They just dropped the name that became synonymous with consultants, certifications, transformation failures, and the enforcement of rituals.

You all know organizations that loudly rejected “Agile” and now quietly practice its core ideas more effectively than any companies running certified transformation programs. The brand failed. The ideas won.

So why are we still fighting about the label?

Free Ebook: 97 Scrum Master Interview Questions to Identify Suitable Candidates

by Stefan Wolpers | 2022-06-17 Featured Agile and Scrum

TL; DR: The Scrum Master Interview Guide to Identify Genuine Scrum Masters

In this comprehensive Scrum Master Interview guide, we delve into 97 critical questions that can help distinguish genuine Scrum Masters from pretenders during interviews. We designed this selection to evaluate the candidates’ theoretical knowledge, practical experience, and ability to apply general Scrum and “Agile “principles effectively in real-world scenarios—as outlined in the Scrum Guide or the Agile Manifesto. Ideal for hiring managers, HR professionals, and future Scrum teammates, this guide provides a toolkit to ensure that your next Scrum Master hire is truly qualified, enhancing your team’s agility and productivity.

If you are a Scrum Master currently looking for a new position, please check out the “Preparing for Your Scrum Master Interview as a Candidate” section below.

So far, this Scrum Master interview guide has been downloaded more than 25,000 times.

Scrum Master Interview — How to Prepare Yourself to Stand Out — Age-of-Product.com

Hiring: 92 Scrum Product Owner Interview Questions to Avoid Agile Imposters

by Stefan Wolpers | 2021-09-26 Featured Agile and Scrum

TL; DR: 92 Product Owner Interview Questions to Avoid Imposters

If you are looking to fill a position for a Product Owner in your organization, you may find the following 92 interview questions useful to identify the right candidate. They are derived from my sixteen years of practical experience with XP and Scrum, serving both as Product Owner and Scrum Master and interviewing dozens of Product Owner candidates on behalf of my clients.

So far, this Product Owner interview guide has been downloaded more than 10,000 times.

82 Product Owner Interview Questions to Avoid Imposters — Age-of-Product.com

📅 Upcoming AI 4 Agile Workshops, Scrum Training Classes, and Events

by Stefan Wolpers | 2020-10-10 Featured Workshops

TL; DR: Upcoming AI 4 Agile Workshops, Scrum Training Classes, and Events

Age-of-Product.com’s parent company — Berlin Product People GmbH — offers Scrum training classes authorized by Scrum.org, Liberating Structures workshops, and hybrid training of Professional Scrum and Liberating Structures. The training classes are offered both in English and German.

Check out the upcoming timetable of training classes, workshops, meetups, and other events below and join your peers.

Upcoming Scrum and Liberating Stuctures training classes and workshops — Berlin Product People GmbH

Food for Agile Thought 569: AI’s Götterdämmerung? End of Feature Discovery, End of Product Owners, IKEA’s Smart AI Move

by Stefan Wolpers | 2026-11-06 News

TL; DR: AI’s Götterdämmerung — Food for Agile Thought #569

Welcome to the 569th edition of the Food for Agile Thought newsletter, shared with 35,189 peers.

📺 Short on time? Watch the 69-second teaser video now! (Do not forget to set ‘Quality’ to 1080p or HD.)

This week, one team of authors questions the AI boom: valuations assume 4 to 5 percent productivity growth, yet data shows about 1 percent. Marty Cagan sees AI moving the bottleneck to discovery, whereas Elena Verna wonders whether agents end feature discovery. Pavel Samsonov doubts AI summaries will rescue overloaded users, while Richard Mironov, because agents grant tickets literally, proposes human review. Also, Chad McAllister’s guests, Jay Nakagawa and Bill Reid, treat many trade-offs as solvable contradictions, pairing Jobs-to-be-Done, TRIZ, and AI to speed analysis.

Next, Casey Newton warns Democrats could squander the AI backlash by repeating the social media decade. Tobi Lütke tells Alex Heath that agent startups should nail value before a business model, while Tugce Erten contends cheaper models strengthen customers’ DIY option, which applications must outperform. As models improve, Sangeet Paul Choudary sees the reviewer role shrinking, and Addy Osmani recommends cutting outdated instructions, whereas Steve Yegge’s cheap models earn autonomy against human baselines.

Lastly, Eric Ries shows IKEA retraining 8,500 workers its chatbot replaced, after which remote sales grew fastest, and Jerry Colonna denies that replacing executives fixes teams replaying inherited roles. John Cutler wonders whether sabotaging ourselves protects our values, while Evgeny Kaganer and Christoph Loch favor evolving learning journeys over fixed transformation programs, and Tom Bennet ends PMs’ clipboard duty between tools. Finally, Paweł Huryn’s 43 skills let product people build with coding agents without reading code.

Food for Agile Thought 569: AI's Götterdämmerung? End of Feature Discovery, End of Product Owners, IKEA's Smart AI Move - Age-of-Product.com

Measuring AI Impact: What Your Velocity Chart Cannot Tell the CFO

by Stefan Wolpers | 2026-11-01 Agile and ScrumAgile Transition

TL;DR: What Your Velocity Chart Cannot Tell the CFO

Sooner or later, someone in your organization shows a slide with a number about what AI did for your team: typically, the team’s “velocity” before and after the coding agents arrived, plus a percentage that looks like a return on the token bill. The number may even be “correct”; however, it answers a different question than the one finance is asking: A before-and-after comparison credits every change in that period to the agents, and measuring velocity is controversial for good reason under normal circumstances, which makes measuring AI impact by this means even more challenging.

A claim your team can defend starts elsewhere: with an honest grade of the evidence, a break-even test for every delegated AI workflow, and the creep check of the AI Delegation Audit, which catches the workflows that pass the test only because nobody reviews their output anymore. Each workflow then gets a keep, change, or retire decision in the Re-classification Log, and the result fits on one page that states what the team can claim and what it cannot. Bonus: For your own work, a small trial of eight to ten runs gives you a direction you can act on.

📺 Watch the article’s video: Measuring AI Impact: What Your Velocity Chart Cannot Tell the CFO.

Measuring AI Impact: What Your Velocity Chart Cannot Tell the CFO - by Stefan Wolpers of Age-of-Product.com.

Thesis: This article explains how agile teams can measure AI impact effectively, why before-and-after velocity charts mislead, the three grades of evidence, a break-even test for delegated workflows, a one-page statement for finance, and a personal trial.

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 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 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.