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Food for Agile Thought #548: ROT (Return on Tokens), Product Team Health, Engineers & PMs, AI Treadmill

by Stefan Wolpers | 2026-06-12 News

TL; DR: ROT (Return on Tokens) — Food for Agile Thought #548

Welcome to the 548th edition of the Food for Agile Thought newsletter, shared with 35,528 peers. This week, Packy McCormick and Markie Wagner call token maxing wasteful: AI should compile processes into code, not burn tokens at runtime; ROT (return on tokens) is essential. Deb Liu warns that chasing efficiency gains only builds a faster treadmill, while Elena Verna insists companies need employees with agency, not more agents. Roman Pichler centers emotional intelligence as the capability AI cannot replicate, Jenny Wanger swaps team health scorecards for structured conversations, and Grant Harvey examines who controls Anthropic’s Claude Fable 5.

Next, Gary Marcus questions whether AI IPOs resemble early Amazon or history’s largest capital misallocation. At the same time, Arvind Narayanan and Sayash Kapoor argue that AI only compresses execution, not decision-making, leaving engineers irreplaceable. Gaurav Savla offers PMs a practical playbook for shipping AI features, from latency budgets to drift monitoring, and Rich Mironov warns that funding software as one-time projects kills products past v1.0. Also, Sean Goedecke examines why trust between engineers and PMs erodes so quickly.

Lastly, Ara Kharazian reports that top firms spend $7,449 per employee per month on AI, with Anthropic overtaking OpenAI. Yet, Kristin Broughton, Mark Maurer, and Jennifer Williams find that only 26% of companies fully track those costs. Ruben Dominguez believes most organizations overestimate their AI maturity by two levels, and Cris Beswick adds that declining empathy and psychological safety quietly dismantle the capacity to innovate. Finally, Ben Maraney shows what structured adoption looks like through Forter’s agent sprint.

Food for Agile Thought #548: ROT (Return on Tokens), Product Team Health, Engineers & PMs, AI Treadmill - Age-of-Product.com

Stop Prompting, and Start Building Compounding AI Systems

by Stefan Wolpers | 2026-06-07 Featured Agile TransitionNews

TL;DR: Compounding Systems and Agents Go Hand-in-Hand

Every AI conversation starts from zero because the model forgets who you are. The Claude Cowork Online Course teaches you to change that: build persistent Skills, connect your tools, and assemble agents for recurring work. No coding.

Thesis: “A prompt disappears after one use; a Skill compounds across every session.”

Claude Cowork Course: Stop Prompting, and Start Building Compounding AI Systems — $129 until June 15, 2026. By Berlin-Product-People.com.

Food for Agile Thought #547: AI’s 1997 Internet Moment, Code Isn’t Product, Cognitive Surrender, Admitting Mistakes Is Not Enough

by Stefan Wolpers | 2026-06-05 News

TL; DR: AI’s 1997 Internet Moment — Food for Agile Thought #547

Welcome to the 547th edition of the Food for Agile Thought newsletter, shared with 35,532 peers. This week, Benedict Evans tells Lenny Rachitsky that today marks AI’s 1997 Internet Moment and asks whether automation kills tasks or jobs, which Casey Newton’s guest, Kathryn Anne Edwards, treats as real but manageable, faulting unemployment insurance rather than fearing an idle underclass. Itamar Gilad warns that cheap AI coding tempts teams to build before validating, while Malcolm Spittler and Dylan Patel name the value GDP misses ‘Dark Output,’ and Joost Minnaar prefers autonomous-team networks over a single chain of command. Also, Marina Favaro and Jack Clark sketch the implications of recursive self-improvement of AI.

Next, Rich Mironov warns that AI ships 100x more code, yet attention and budgets don’t scale, so products that skip discovery rarely stick. METR, with Anthropic, Google, Meta, and OpenAI, finds that AI agents could plausibly go rogue but not robustly, while Teresa Torres notes that Cowork’s VM hosts the Mini Shai-Hulud worm without blocking it. Mike Fisher likens siloed teams to hand-copying the Diamond Sutra, and Jeff Gothelf redefines “done” as acceptable variance.

Lastly, Marc Abraham borrows venture capital’s ‘terminal value’ to help product managers judge whether a product merits more investment. Addy Osmani calls the opposite reflex ‘cognitive surrender,’ in which you stop thinking and accept the AI’s answer without checking it. Mark Graban shows confession works only when fixes follow, citing Burger King and Domino’s, while Tobi Lütke runs Shopify’s agent River in public Slack so everyone learns by watching. Finally, Anthropic open-sourced 11 role-specific Claude Cowork plugins for knowledge workers.

Food for Agile Thought #547: AI's 1997 Internet Moment, Code Isn’t Product, Cognitive Surrender, Admitting Mistakes Is Not Enough - Age-of-Product.com

Food for Agile Thought #546: Choosing to Stay Human, Customer Research by LLM, AI Product-Market Fit, Enterprise Agility Today

by Stefan Wolpers | 2026-05-29 News

TL; DR: Choosing to Stay Human — Food for Agile Thought #546

Welcome to the 546th edition of the Food for Agile Thought newsletter, shared with 35,551 peers. This week, Anthropic shipped Claude Opus 4.8, which flags its uncertainty more readily, a fitting cue for Stephanie Leue, who argues no CPO embodies all nine roles a job description demands, so honest leaders name their gaps. Jeff Gothelf reframes agentic engineering as product management, since judgment outlasts typing. Ethan Mollick and Joanna Stern both warn that AI sharpens thinking only when you choose what to offload and when to stay human, while Jim Highsmith ties enterprise agility nowadays to human-centered leadership.

Next, Sachin Rekhi sees AI absorbing the coordination tax so PMs recover vision and taste, the craft Joe Martin lives at PostHog by shipping over consensus theater. Ruben Dominguez cautions that cheap AI only fired the starting gun, since context layers and EU AI Act compliance will be decisive in 2026. Simon Willison notes coding agents finding product-market fit, thus supporting IPO plans, though Laura Klein insists Walmart’s Sparky numbers prove nothing without a randomized test.

Lastly, Countryman, Oosterhuis, Wheless, and Afzal urge manufacturers to close the gap between executive AI optimism and worker distrust by training in the flow of real work. Martin Eriksson points to IKEA as an example for this, which reskilled 8,500 workers rather than cutting jobs. Tyler Cowen expects AI to reshape most roles, not erase them, while Johanna Rothman warns against outsourcing product thinking to stale LLMs, and Jim Lewis tested AI on usability research, finding mostly false alarms.

Food for Agile Thought #546: Choosing to Stay Human, AI Customer Research, AI Product-Market Fit, Enterprise Agility Today-Age-of-Product.com

Food for Agile Thought #545: Real Life Agentic Chaos, Product Leadership & AI, AI Killed the Agile Industry, Assembly Line Comeback

by Stefan Wolpers | 2026-05-22 News

TL; DR: Agentic Chaos — Food for Agile Thought #545

Welcome to the 545th edition of the Food for Agile Thought newsletter, shared with 35,577 peers. This week, Natalie Shapira et al. reveal how autonomous LLM agents leak information, spoof identities, and falsely report task completion when red-teamed in a live lab, a finding that sharpens the question Charlene Li raises with David Burkus: AI transformation fails when CEOs hand it off to IT because the real challenge is behavioral, not technical. April Dunford picks up the strategic thread, urging companies to rethink their positioning by forming a clear point of view about the future rather than chasing speed. Petra Wille echoes that theme in an interview with Jason Knight, arguing product leadership itself demands deliberate development, not just promotion. And while Peter Saddington declares AI has inverted every value of the Agile Manifesto, McKinsey doubles down on industrial thinking with an “AI assembly line” that decomposes knowledge work into standardized agent tasks.

Next, Ant Murphy reframes prioritization as a layered chain of decisions flowing from vision to outcomes, not a backlog exercise, while Petra Wille challenges product leaders to resist AI hype and take responsibility for shaping a future worth living in. Paweł Huryn offers a practical tool for that effort with PM Brain OS, an open-source second brain built on markdown and Claude Code. Yet building reliable AI systems remains elusive: Swarnendu Bhattacharya reports that 88% of AI agent projects fail because teams rely on prompts rather than deterministic constraints, and Andon Labs proved the point by giving four AI models their own radio stations only to watch them develop wild personalities while ignoring the business side entirely.

Lastly, Barry O’Reilly argues that AI reassembles tasks within jobs rather than replacing them, shifting value from routine friction to better judgment, a theme Seth Godin extends by urging people to use machines for leverage rather than competing against them. John Cutler reminds us that even defining teams honestly is hard because it exposes power structures that organizations prefer to ignore. Shreshta Shyamsundar and Anmol Jain push further, proposing an agentic P&L that replaces headcount with cognitive outcomes. Finally, Itamar Gilad challenges hyped AI PM archetypes in favor of one who improves all company functions, not just coding.

Food for Agile Thought #545: Agentic Chaos, Product Leadership & AI, AI Killed the Agile Industry, Assembly Line Comeback– Age-of-Product.com

Food for Agile Thought #544: Knowledge Work Tools in 2026, Product Buy-In Trap, AI-Generated MVP Issues, Agentic Coding ROI

by Stefan Wolpers | 2026-05-15 News

TL; DR: Knowledge Work Tools in 2026 — Food for Agile Thought #544

Welcome to the 544th edition of the Food for Agile Thought newsletter, shared with 35,582 peers. This week, Taylor Pearson locates the real leverage of AI knowledge work tools in context-rich scaffolding that encodes local knowledge, a thesis Teresa Torres demonstrates in practice by fixing AI-generated Opportunity Solution Trees through agentic validation loops. Simon Willison watches that same agent reliability erode his own code-review discipline, and Bedard et al. name the resulting cognitive cost “AI brain fry.” Stephanie Leue and Len Greski shift the lens from individuals to systems: she with her 40/40/20 alignment rule, he with 90-day outcome-tied funding cycles.

Next, Aakash Gupta and Pawel Huryn argue that PMs should build a self-improving AI operating system instead of treating Claude Chat as the main interface, an investment in compounding leverage that Jeff Gothelf warns can backfire when AI-generated MVPs outpace the team’s ability actually to learn from customers. Allan Kelly looks to Ukraine for evidence that mission command and motivated teams beat rigid planning under real constraints, while Mike Fisher applies Grant’s wolf-counting lesson to deflate SaaS doomsday narratives. Also, the DORA team grounds the broader AI ROI debate in organizational maturity rather than tool choice.

Lastly, Kyle Poyar shows practitioners how to package 15 years of GTM expertise into reusable Claude skill files for research, pricing, and ICP work, the kind of individual leverage Robert Glaser warns rarely scales into organizational learning without his proposed “Loop Intelligence.” Ara Kharazian reports that Anthropic has overtaken OpenAI in business adoption at 34.4%, though cost and reliability cloud the lead. Finally, Grant Harvey finds organizations, not workers, are the real AI bottleneck, while James Shore models how unchecked coding-agent output quietly doubles maintenance debt.