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Food for Agile Thought 564: AI Pace Accelerating, Product Trio Future, Career Traps for Smart People, Aggregation Theory & Agents

by Stefan Wolpers | 2026-10-02 News

TL; DR: AI Pace Accelerating — Food for Agile Thought #564

Welcome to the 564th edition of the Food for Agile Thought newsletter, shared with 35,278 peers. This week, Simon Willison suggests that the current AI pace exceeds even his predictions, while Jenny Wanger believes it has made the product trio single-player, creating product debt in the process. Dan Shipper proposes a labs team expecting to discard 90% of its work, whereas Teresa Torres and Petra Wille treat innovation as a byproduct, not a goal. Ben Thompson suggests whoever owns your agent becomes the ultimate gatekeeper, yet Madeline Renbarger, M. Sriram, and Tom Dotan report even agentic commerce enthusiasts still want control.

Next, Dror Poleg suggests delegation ties his AI glossary together, and Casey Newton shows its price: OpenAI’s Dots saves him two hours but wants your email and bank account. For delegated code, Addy Osmani fears that the engineer approving it without independent checks becomes the moral crumple zone. Arvind Narayanan and Sayash Kapoor propose AI extinction probabilities are guesses wearing numbers, whereas Paweł Huryn’s analysis of AI subscription benefits rests on API list rates: SuperGrok buys 190x its price.

Lastly, John Cutler suggests your virtues lure you into career traps, while Jonny Miller points out that white-knuckled grinding ends in burnout; let go. Tim Ottinger hopes AI kills ticket culture and revives XP, whereas Viktor Cessan proposes automating internal requests only once volume justifies it. Finally, Fran Noto favors pairing over documentation; the latter captures only a fraction of what experienced people know, and Anuja Karnik and Sumeet Gayathri Moghe suggest trust grows in everyday work, not by orchestrated events.

Food for Agile Thought 564: AI Pace Accelerating, Product Trio Future, Career Traps for Smart People, Aggregation Theory for Agents - Age-of-Product.com

The Jobs Matrix for AI: Four Boxes Instead of Forty Tools

by Stefan Wolpers | 2026-09-27 Agile and ScrumAgile Transition

TL;DR: A 2×2 Matrix on AI for Agile Practitioners

You probably think making sense of AI means keeping up with every new tool, agent, and pricing tier. Steve Jobs faced a similar mess at Apple in 1997, with a dozen versions of the Macintosh, and he fixed it with a two-by-two grid on a whiteboard. Almost 30 years later, the “Jobs Matrix for AI” sorts the AI tool market and, more usefully, the AI use cases of Scrum Masters, Product Owners, Agile Coaches, and anyone else in agile product development.

Replace Consumer and Pro with Team and Organization, and Desktop and Portable with Chatbot and Agent, and you get four boxes that show where a use case belongs, what it takes to move it, and which box should stay empty.

The Jobs Matrix for AI: 4 Boxes Instead of 40 Tools for Agile Practitioners on Delegation and Judgment - Age-of-Product.com

Thesis: This article applies Steve Jobs’ 1997 Consumer/Pro, Desktop/Portable matrix first to the 2026 AI tool market, and then to agile practices, producing a 2×2 matrix that sorts AI use cases by required decision rights and by delegated authority.

Food for Agile Thought 563: Survival of High-impact Ideas, Friction Is Good, Surrendering Agency to AI, AI in Dysfunctional Systems

by Stefan Wolpers | 2026-09-25 News

TL; DR: Survival of High-impact Ideas — Food for Agile Thought #563

Welcome to the 563rd edition of the Food for Agile Thought newsletter, shared with 35,293 peers. This week, Jason Knight and Rich Mironov explore why faster AI-generated code does not create faster revenue, and Itamar Gilad sees a similar limit: output-maxing won’t rescue high-impact ideas from prioritization politics. John Cutler believes AI strips the positive friction forcing teams to think, while Roman Pichler suggests its sprawl lets teams hit targets as the business stagnates. Martin Eriksson would assumption-map leadership’s untested strategy, whereas Pavel Samsonov believes “Claude wrote it” launders slop.

Next, Artificial Analysis finds Claude Opus 5.5 tops its Intelligence Index, while Simon Willison reports that Opus on “Max” thinks so hard it never delivers the Pelican, Simon’s famous test. Addy Osmani suggests deleting “think carefully” prompts and naming when to stop. Diogo Almeida wants models to return typed decisions instead of text, creating Jev, while Charity Majors suggests chatbot answers erode trust when colleagues want your opinion. Also, Mustafa Suleyman believes Anthropic’s constitution fuels anthropomorphism and makes containing AI harder.

Lastly, Nigel Thurlow and Mike Fisher learn from Toyota: Thurlow believes AI visibility changes nothing unless leaders act, while Fisher shows performance is largely systemic. Tim O’Reilly likewise believes going AI native is a human problem. Steve Blank watched day-one AI demos turn MVPs into evidence theater, and James Shore believes AI speed gains can evaporate. Finally, Jeff Gothelf suggests key results track the humans deploying or receiving an agent’s work, not API calls.

Food for Agile Thought 563: High-impact Ideas, Friction Is Good, Surrendering Agency to AI, AI in Dysfunctional Systems - Age-of-Product.com

AI on Top of a Dysfunctional System: The Product Backlog

by Stefan Wolpers | 2026-09-20 Agile and ScrumAgile Transition

TL;DR: Polished Artifacts, Unchanged Decisions, Or Ten Backlog Anti-Patterns AI Makes Worse

Add AI to a Product Backlog process that already struggles, and everything seems to improve within an afternoon. The problem is that polishing artifacts with AI doesn’t fix the root cause: the basis for the team’s decisions doesn’t change; AI only removes the visible discomfort that used to signal something was broken, along with some of the pressure to fix it. AI applied to a dysfunctional system – here, the Product Backlog process – makes the dysfunction look like progress.

This is the first article of a new series that explains the uselessness of bolting AI onto a dysfunctional system. It explains why generative AI worsens ten Product Backlog anti-patterns, how polished AI artifacts can hide missing evidence and authority, and how teams can check whether their process is fit for AI.

The example anti-patterns accelerated by AI are from my Scrum Anti-Patterns Guide book.

AI on Top of a Dysfunctional System: 10 Product Backlog Anti-Patterns AI Makes Worse – by Stefan Wolpers of Age-of-Product.com

Thesis: Adding AI onto a dysfunctional system, for example, the Product Backlog, makes ten typical backlog anti-patterns worse, because polished AI artifacts hide missing customer evidence, decision authority, and feedback; the article explains these mechanisms, their costs, and how teams test whether their process is ready for AI.

Food for Agile Thought 562: Slop Grenades, AI Didn’t Make Delivery Free, Pacing the Frontier, Handling Disruptive Change

by Stefan Wolpers | 2026-09-18 News

TL; DR: Slop Grenades — Food for Agile Thought #562

Welcome to the 562nd edition of the Food for Agile Thought newsletter, shared with 35,321 peers. This week, Tobi Lütke tells Shane Parrish that AI makes judgment more valuable because machines cannot take responsibility. Teresa Torres, Petra Wille, and Josh Elman add that AI cheapens features and demos, yet working products still demand judgment. Barry O’Reilly makes that judgment auditable before deployment through baselines and accountable owners. Also, Dario Amodei applies that accountability to the labs with third-party evaluators and speed limits, while Dwarkesh Patel’s guests expect expert-beating AI within ten years.

Next, Nigel Thurlow argues that we turned Lean into belts and templates, although Toyota wanted a better management system. Refinement repeats that mistake: Maarten Dalmijn untangles its four parallel conversations, and Mike Cohn warns that polished AI stories hide gaps. AI adoption needs the same management focus: Aaron De Smet ties it to trust, Christiaan Verwijs and Barry Overeem recruit volunteers for Reflexive AI, Addy Osmani explains how unsupervised agents turn legacy code into technical debt factories.

Lastly, Cedric Chin argues that structural constraints, not stubbornness, kill incumbents, and Elena Verna catalogs the sales habits that smother PLG. Matthew Hodgson applies that logic to AI, where functional silos swallow productivity gains unless leaders fix the operating model before buying tools. Also, Eliezer Yudkowsky explains why models ignore instructions: a polite ambassador talks while a doer chases rewards. Finally, Marily Nika plans for that gap with Minimum Viable Quality: rank failure modes and design for recovery.

Food for Agile Thought 562: Slop Grenades, AI Didn't Make Delivery Free, Pacing the Frontier, Disruptive Change - Age-of-Product.com

The AI Workflow Inventory: Can Your Team Name the Work It Already Runs With AI?

by Stefan Wolpers | 2026-09-13 Agile and ScrumAgile Transition

TL;DR: The AI Workflow Inventory Finalizes the A3 Delegation System

You probably know your own AI shortcuts: the colleague who drafts the stakeholder update, the nightly job somebody set up before they left, the interview notes that go through a model on demand. However, I want to challenge you: that perceived knowledge can create false confidence, because it feels like knowing the team’s way of working with AI, when it is still only a diffuse understanding of the practice. Ask the team to combine those individual accounts into one list, and you may discover how little of the whole anyone can see. The AI Workflow Inventory is the artifact for that list: one row per recurring task, refined into provisional task classes to prepare the team’s next AI delegation decisions.

This is another post from the series on building the A3 Delegation System in public. (I gladly answer all questions you have, and yes, I am considering creating an A3 Delegation application.)

The AI Workflow Inventory of the AI Delegation System: Can Your Team Name the Work It Already Runs With AI? - Age-of-Product.com

Thesis: The AI Workflow Inventory is a one-page canvas listing every recurring task a team already runs with AI, grouped into provisional task classes. It is A3 Delegation System’s seventh artifact. This article explains how to build it in one 60-minute session.