Explore 126 articles and insights organized by topics and expertise areas.
How Brian Balfour’s Four Fits framework has been updated for the AI era, and what product leaders can learn from the shift.
99 posts in 100 days: patterns that emerged, AI's surprising dominance, and 5 posts to start with if you're new here. Reflection on daily writing.
Why do customers churn despite green dashboards? Three layers determine retention: friction (anger), outcomes (indifference), resonance (loyalty).
How product managers can move beyond feature roadmaps to build strategy-driven outcomes, handle stakeholder pressure, and make sharper prioritization decisions.
How leading product teams use betting principles to make smarter decisions, test ideas fast, and adapt quickly to real-world results.
Product leaders must know when to act as Architects and when to act as Gardeners. Learn how to balance precision and adaptability in product development.
Product managers are evolving from process coordinators to augmented architects: AI-powered builders who own strategy and commercial outcomes.
Internal platforms that slow teams down are taxes. Those that accelerate them are multipliers. The mandate trap hides which one you're building.
Why do customers churn despite green dashboards? Three layers determine retention: friction (anger), outcomes (indifference), resonance (loyalty).
Google trained Gemini 3 entirely on TPUs, bypassing NVIDIA's tax. The margin war between vertical integration and the CUDA ecosystem begins.
Waiting isn't friction to eliminate. It's captive attention begging for engagement. The best products turn loading screens into learning moments.
AI accelerates shipping but not learning. Teams build faster without validating if they're solving the right customer problems.
AI isn't eliminating product roles wholesale. It's commoditizing entry-level work while amplifying senior value, hollowing out the career ladder.
AI agents excel at coding but fail at business tasks. The gap reveals what's missing: learning from experience and error recovery.
Three major outages expose the gap between multi-cloud architecture and actual resilience when CDN infrastructure fails.
LLMs excel at language. World models learn by watching: understanding space, time, and physics. I'm tracking why this matters for product builders.
AI agents boost output but multiply review work while threatening entry-level jobs. Two patterns reshaping knowledge work simultaneously.
Google's whitepaper shows why stateful AI requires engineering context with Sessions and Memory, not just better prompts.
Enterprises repeat goals endlessly but skip strategy. In the AI era, that gap between knowing the destination and coordinating the route is existential.
Velocity isn't about shipping more features. It's about running faster learning loops that turn uncertainty into validated decisions.
Retention problems are created in week one, not month six. Product decisions about time-to-value determine long-term stickiness.
Anthropic's Artifacts tests user-paid sharing where creators pay nothing for distribution while users burn their own credits.
Prototyping tools like Claude Code now serve both discovery and production, narrowing the gap between build-to-learn and build-to-earn.
Innovation labs fail when they isolate thinking. The companies winning are the ones where core business teams have built the innovation muscle.
Two-thirds of organizations are stuck in AI pilot phase. The gap between adoption and enterprise value isn't technology—it's redesigning workflows.
Your competitor added 10,000 customers. You added 200 developers. Who wins? Ecosystem dependency beats user acquisition every time.
Everyone's racing to add AI features. Few are building AI moats. The difference determines who's still competitive in 18 months.
Product sense isn't magic—it's systematic practice. Learn how to build intuition through decision-making, user empathy, and pattern recognition.
AI agents transform knowledge work from execution to management. ICs need allocation and judgment skills, not just execution speed.
Webflow's AI search data reveals why aggregate traffic is misleading and what product teams should measure instead.
Daily publishing needs a scalable taxonomy. Claude Code built mine in 45 minutes: 63 tags → 40, three tiers, full automation.
Managers must add more value than they cost. Apply customer-thinking to direct reports: justify your existence through real services.
99 posts in 100 days: patterns that emerged, AI's surprising dominance, and 5 posts to start with if you're new here. Reflection on daily writing.
Exploring a B2B GTM survey through a PM lens: two data points that might change how you think about pricing and AI features.
Meta's Early Experience research explores agents learning from their own rollouts. Early results look promising—here's what changes if it scales.
The gap isn't whether AI agents work—it's who can deploy them. Infrastructure inequality is creating two types of organizations. Act now.
A simple framework to evaluate product impact: map your work by customer value and business value to focus on what matters most.
Anthropic’s enterprise and life sciences focus shows how AI companies are testing different business paths: one broad, one deep toward sustainable growth.
How product teams can avoid paralysis in the AI era by acting before the window closes and minimizing the cost of delay.
AI is commoditizing your competitive advantage. Three strategic paths exist: race to the top, bottom, or adjacent. Choose deliberately or fail.
A practical, no-hype guide for project managers moving into product roles — how to shift from delivery to discovery, unlearn old habits, and build learning loops that compound insight.
AI search consolidates power across three layers: intent capture, routing, and monetization. The market structure implications are explained.
OpenAI’s Atlas browser isn’t a surprise, but it might be a signal. This post explores how Atlas fits into OpenAI’s evolving ecosystem and why the interface-to-platform shift matters.
Claude Skills let users package reusable workflows that make Claude adaptable and modular: a practical leap in how AI assistants work.
AI isn’t replacing human help—it’s redefining it. History shows that every new technology first sparks fear, then expands what humans can do.
AI can now take product teams from 50 to 90 faster than ever, but the final 10 still belongs to human intuition, judgment, and empathy.
AI-assisted prototyping is reshaping collaboration across product, design, and engineering—accelerating discovery and demanding the best from each discipline.
Established companies can balance today’s business with tomorrow’s AI potential by sequencing AI-enabled and AI-native strategies.
AI agents don’t replace jobs—they expand what teams can achieve. Learn how product leaders can turn automation gains into growth opportunities.
Spotify’s “bets board” shows how leaders can treat decisions as experiments. Here’s how to explore that mindset without copying the model.
Evals make AI products measurable. Traces, annotations, and layered tests turn evaluation into a practical loop for reliability.
Claude Sonnet 4.5 introduces usage tracking and thinking mode enhancements that streamline AI planning and session management.
How product managers can move beyond feature roadmaps to build strategy-driven outcomes, handle stakeholder pressure, and make sharper prioritization decisions.
AI makes building faster, but not smarter. The new PM advantage is clarity, knowing what to test, why it matters, and how to learn fast.
Sam Altman outlines how OpenAI plans to turn ChatGPT into the internet’s next interface, powered by apps, commerce, and global infrastructure growth.
Notes on strategy, speed, and why modern product leadership is a leverage game.