Posts tagged "Product Strategy"

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Your platform is either a tax or a multiplier

Internal platforms that slow teams down are taxes. Those that accelerate them are multipliers. The mandate trap hides which one you're building.

Internal product managers often believe a dangerous myth. They think they don't have to worry about churn. Since the company mandates the use of their API gateway, design system, or data warehouse, they assume their user base is guaranteed. They have a captive audience. But in platform product management, users don't churn. They rot. When users are forced to use a tool they hate, they engage in malicious compliance. They do the bare minimum. They...
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Customer satisfaction is a hierarchy, not a metric

Why do customers churn despite green dashboards? Three layers determine retention: friction (anger), outcomes (indifference), resonance (loyalty).

We have all been in that strategy meeting. The dashboard is green. Uptime is 99.9%, support ticket volume is down, roadmap is on schedule. And yet, customers are churning. The problem isn't the data. It's the definition. We treat "customer satisfaction" as a single bucket. We dump everything into it: bug fixes, new features, polite support emails, brand colors. If the bucket is full, we assume we are winning. But satisfaction isn't a bucket. It's...
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Gemini 3 Proves the NVIDIA Tax Is Optional

Google trained Gemini 3 entirely on TPUs, bypassing NVIDIA's tax. The margin war between vertical integration and the CUDA ecosystem begins.

Google's Gemini 3 landed last week with impressive reviews: frontier-class performance that beats OpenAI and Anthropic except for its agentic code. Conventional wisdom said Google was lagging behind OpenAI, which remains true in adoption. But on capability, they have a real chance to catch up. The tech press is focused on benchmarks and capabilities. They're missing the real headline: Google trained these models entirely on TPUs. Zero NVIDIA dependency. While competitors debate model quality, Google...
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Dead Time Is Story Time

Waiting isn't friction to eliminate. It's captive attention begging for engagement. The best products turn loading screens into learning moments.

I stumbled on David Maister's 1985 research on waiting psychology while thinking about loading screens. His foundational principle: occupied time feels shorter than unoccupied time. But he uncovered something deeper. Anxiety, uncertainty, and unexplained delays make waits feel exponentially longer. That got me thinking. Most product teams treat waiting as friction to eliminate. Load faster. Reduce checkout steps. Skip the queue. But you can't eliminate all dead time. Users will wait during loading, onboarding, checkout,...
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The Feature Factory Problem AI Amplifies

AI accelerates shipping but not learning. Teams build faster without validating if they're solving the right customer problems.

Your team just shipped three features this week. Last quarter, that would have taken a month. AI tools turned your engineers into feature factories. Your designers generate variants in minutes. Your PMs prototype without waiting for engineering resources. Everyone's celebrating velocity. Who's checking if you're solving the right problems? Creation velocity isn't validation velocity Recent research shows contradictory results on AI's impact. Some studies report significant productivity gains, others find developers actually slow down when...
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AI Commoditizes Entry-Level Work While Amplifying Senior Value

AI isn't eliminating product roles wholesale. It's commoditizing entry-level work while amplifying senior value, hollowing out the career ladder.

Everyone's asking the wrong question about AI and product teams. The debate splits into two camps: one believes product managers will code their way to replacing engineers, the other thinks engineers will own strategy and eliminate PMs. Both narratives miss what's actually happening. AI isn't replacing entire functions. It's splitting each function into three tiers, and only one of them is shrinking. The same pattern across three functions Look at what's happening to engineers first....
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Why AI Agents Fail Today Despite the Hype

AI agents excel at coding but fail at business tasks. The gap reveals what's missing: learning from experience and error recovery.

The agentic AI hype promises autonomous decision-makers that replace employees or dramatically boost efficiency. The reality, according to Maria Sukhareva (Principal AI Expert at Siemens) in "Why AI Agents Disappoint," is that general-purpose AI agents don't work for most real-world business use cases. The WebArena benchmark proves the gap quantitatively. Researchers created realistic web environments (e-commerce sites, forums, development platforms) and asked GPT-4-based agents to complete end-to-end tasks like "Find the cheapest phone case and...
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Infrastructure Redundancy Stops Before the CDN

Three major outages expose the gap between multi-cloud architecture and actual resilience when CDN infrastructure fails.

Azure, AWS, and Cloudflare all experienced significant outages in recent weeks. Different providers, same story: configuration changes triggering cascading failures across infrastructure that's supposed to be resilient. The interesting part isn't that infrastructure fails. It's what gets exposed about the gap between architected resilience and actual resilience. The multi-cloud gap Companies might use AWS for one application and Azure for another, but any given application typically runs on a single cloud. Redundancy within that provider...
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World Models Teach AI to See

LLMs excel at language. World models learn by watching: understanding space, time, and physics. I'm tracking why this matters for product builders.

On Lenny's recent podcast, Fei-Fei Li called LLMs "wordsmiths in the dark": eloquent but ungrounded in physical reality. The phrase resonated because it captures exactly what language models can't do: understand space, navigate environments, predict physics, or reason about the 3D world we inhabit. I've been following world models with growing curiosity. The contrast with LLMs is stark. Where language models learn statistical patterns from text, world models learn by watching: absorbing spatial relationships, temporal...
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AI Agents Multiply Work and Eliminate Jobs Simultaneously

AI agents boost output but multiply review work while threatening entry-level jobs. Two patterns reshaping knowledge work simultaneously.

Traditional automation follows a script. You map the steps, define the logic, and the system executes. If-then-else at scale. AI agents are different. They have decision-making authority. You give them a goal, and they figure out the path, making choices on the fly based on context. That shift from scripted execution to delegated judgment changes what happens to your workload. What the data shows A recent study from Faros AI analyzed over 10,000 developers across...
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Context Engineering Turns AI Agents From Goldfish Into Assistants

Google's whitepaper shows why stateful AI requires engineering context with Sessions and Memory, not just better prompts.

Your AI agent is brilliant. It can write code, analyze documents, and answer complex questions with remarkable sophistication. It is also a goldfish. Every conversation starts from scratch. Every user is a stranger. Every context is new. Google just released a whitepaper on context engineering that tackles this fundamental problem. The paper introduces a systematic framework for making LLM agents stateful using two core primitives: Sessions and Memory. The framework formalizes the architectural patterns that...
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Goal Clarity Without Strategy Clarity Is Just Noise

Enterprises repeat goals endlessly but skip strategy. In the AI era, that gap between knowing the destination and coordinating the route is existential.

The dynamic is shifting. AI tools let startups go from idea to credible prototype in weeks, not quarters. Technical execution gaps are narrowing. For enterprises, this changes the calculus. The advantages used to be resources, data, distribution, and customer relationships. Those still matter. But only if you can deploy them before the market moves. The real enterprise problem isn't speed It's coordination. Everyone knows the goal. "AI transformation." "Double growth." "Modernize the platform." Leadership repeats...
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Why Retention Starts at Onboarding, Not Growth

Retention problems are created in week one, not month six. Product decisions about time-to-value determine long-term stickiness.

Most products lose 80% of users within 30 days. Teams see this happening and hand the problem to growth. They add email campaigns, push notifications, re-engagement hooks. None of it moves the number because the retention problem wasn't created in month six. It was locked in during week one. This isn't about better onboarding flows or slicker tutorials. It's about product decisions made before launch that determine whether users stay or leave months later. By...
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Why AI Platforms Are Testing User-Paid Sharing

Anthropic's Artifacts tests user-paid sharing where creators pay nothing for distribution while users burn their own credits.

Most platforms face a brutal tradeoff when enabling sharing. Charge creators for hosting and you limit adoption. Charge end-users at the point of distribution and you create friction. Subsidize usage yourself and the costs don't scale. Each path blocks something you need: viral growth, sustainable economics, or both. For years, platforms have picked their poison. SaaS tools charge creators monthly fees, killing casual sharing. Consumer apps eat infrastructure costs to drive growth, then scramble to...
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Don't Contain Innovation—Spread It

Innovation labs fail when they isolate thinking. The companies winning are the ones where core business teams have built the innovation muscle.

Your innovation lab isn't the problem. Keeping innovation isolated there is. The pattern shows up in different forms—innovation labs, Centers of Excellence, digital transformation teams. The setup looks similar: bring in smart people, give them freedom to experiment, then wait for breakthroughs while the core business operates exactly as before. I've watched this play out at multiple Fortune 500 companies. The lab discovers what customers need. Core business teams keep building the same way they...
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The Gap Between AI Adoption and Enterprise Value

Two-thirds of organizations are stuck in AI pilot phase. The gap between adoption and enterprise value isn't technology—it's redesigning workflows.

Two-thirds of organizations are stuck in the pilot phase with AI. They run experiments, they test use cases, they see promising results—then nothing scales. McKinsey's latest State of AI report (November 2025) reveals the pattern: 90% of organizations regularly use AI, but only 39% report enterprise-level EBIT impact. The gap between adoption and value isn't a mystery. It's a choice. What the data shows The survey of organizations reveals three distinct clusters: Most organizations (nearly...
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Count Dependency, Not Customers

Your competitor added 10,000 customers. You added 200 developers. Who wins? Ecosystem dependency beats user acquisition every time.

Your competitor just announced 10,000 new customers. You added 200 developers to your API program. Who wins? Traditional B2B thinking says the customer count matters most. Ecosystem thinking says dependency beats scale every time. The moat has moved For decades, B2B competitive advantage meant user acquisition. More customers, more revenue, stronger position. Simple math. That math doesn't work anymore. The $420B business SaaS market is reorganizing around a different principle: the companies that provide infrastructure...
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Feature Factories Build AI Wrappers, Product Orgs Build Moats

Everyone's racing to add AI features. Few are building AI moats. The difference determines who's still competitive in 18 months.

Every company can call the OpenAI API. Every developer can wrap Claude in a decent UI. Every product team can ship "AI-powered" features in a sprint or two. The hard part isn't adding AI. It's building something users can't easily replicate elsewhere. The Wrapper Trap Integrating GPT-5 or Claude takes a weekend. Polish the UI, tune some prompts, add it to your feature list. Congratulations—you've built what a hundred competitors can build in the same...
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Traffic Metrics Are Lying to You

Webflow's AI search data reveals why aggregate traffic is misleading and what product teams should measure instead.

Your traffic is down. Your growth team is panicking. And your product metrics might be telling you absolutely nothing useful. Kyle Poyar's 2025 State of B2B GTM report uncovered something fascinating: Webflow's aggregate traffic is declining while their business is accelerating. ChatGPT referrals convert at 24% compared to 4% from Google. Two-thirds convert within 7 days. This isn't a Webflow-specific anomaly. It's what happens when AI search reshapes discovery. The death of aggregate traffic as...
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What Emerged: 99 Days of Product Thinking Journal

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.

One hundred posts. For me, writing is clarifying thinking. I built this entire site with Claude Code—designed it, deployed it, automated the Obsidian-to-Cloudflare publishing flow. Now, sixty percent of what I've written is about AI. The tool became the subject. That's either profound or obvious, depending on your tolerance for meta-commentary. Here's what I didn't expect: not the daily writing (I'm reading widely anyway, so ideas only compound), but the sheer pace at which AI...
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Two GTM Insights Product Managers Can Actually Use

Exploring a B2B GTM survey through a PM lens: two data points that might change how you think about pricing and AI features.

I've been digging through the 2025 State of B2B GTM Report from Growth Unhinged, and while most of it focuses on channel strategy and GTM execution, two findings stood out for their direct relevance to product work. These aren't prescriptions—they're observations from one dataset that might be useful as you think about your own product decisions. Your pricing tier predicts your GTM motion (not the other way around) The survey shows clear patterns between product...
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Early Experience: A Different Approach to Agent Training

Meta's Early Experience research explores agents learning from their own rollouts. Early results look promising—here's what changes if it scales.

AI agents are currently in use, handling customer service interactions, automating research workflows, and navigating complex software environments. But training them remains resource-intensive: you either need comprehensive expert demonstrations or the ability to define clear rewards at every decision point. Meta's recent research explores a third path. Agent Learning via Early Experience proposes agents that learn from their own rollouts—without exhaustive expert coverage or explicit reward functions. It's early, but the direction is worth understanding....
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Agentic AI: It's the Readiness and Access Story

The gap isn't whether AI agents work—it's who can deploy them. Infrastructure inequality is creating two types of organizations. Act now.

The gap between hype and reality isn't the story everyone's missing about agentic AI. The gap between who's positioned to deploy it and who's stuck waiting for infrastructure—that's the story. And that gap is widening every quarter. The technology is proven—access to it is not Nearly every senior enterprise developer is experimenting with AI agents right now. One in four enterprises is deploying them across teams this year. The question isn't whether autonomous AI systems...
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The Impact Scorecard

A simple framework to evaluate product impact: map your work by customer value and business value to focus on what matters most.

It's surprisingly easy to stay busy without making much of an impact. A team ships features, hits sprint goals, and sees metrics move—but six months later, it's unclear what actually mattered. Not because the team wasn't working hard, but because "impact" is slippery to define. I've found it helpful to think about impact along two dimensions: customer value and business value. When you map your work on both axes, patterns start to emerge about what's...
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Anthropic’s Quiet Advantage in the AI Race

Anthropic’s enterprise and life sciences focus shows how AI companies are testing different business paths: one broad, one deep toward sustainable growth.

“Anthropic’s growth path is a lot easier to understand than OpenAI’s. Corporate customers are devising a plethora of money-saving uses for AI in areas like coding, drafting legal documents, and expediting billing.” — The Wall Street Journal That line captures an important dynamic in today’s AI market: two companies building similar technology, but betting on very different ways to make it sustainable. OpenAI is chasing scale — hundreds of millions of users, a consumer-facing brand,...
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When the Cost of Delay Becomes Your Biggest Risk

How product teams can avoid paralysis in the AI era by acting before the window closes and minimizing the cost of delay.

Blockbuster had every advantage—brand, reach, loyal customers. They saw Netflix coming and had the resources to compete. They waited too long. The window closed. Kodak invented the digital camera in 1975. They knew film was vulnerable. They protected margins instead of building the future. When they finally moved, others had already won. BlackBerry watched the iPhone launch and dismissed touchscreens as toys. They waited for validation. The window closed again. The pattern is clear: in...
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AI Commoditization and Three Strategic Paths

AI is commoditizing your competitive advantage. Three strategic paths exist: race to the top, bottom, or adjacent. Choose deliberately or fail.

AI makes your differentiator table stakes. Your competitive advantage is evaporating. The junior employee with AI tools matches the senior expert's output. The expertise that took years to build becomes a commodity. What do you do when your moat becomes a puddle? The framework Three strategic paths exist. Each works. Each requires different capabilities. Race to the top: Invest in capabilities AI can't commoditize. This works when you can build compounding advantages, such as proprietary...
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It's not a Search Problem, it's a Distribution Problem

AI search consolidates power across three layers: intent capture, routing, and monetization. The market structure implications are explained.

Yesterday, I discussed how Atlas builds OpenAI's interface-to-platform flywheel through continuity of context. Today, it's an extension: the market structure implications of a company controlling all three layers of distribution. The conversation about AI search misses the real shift. This isn't about which tool delivers better results. It's about who controls the starting point for online activity and whether that control consolidates or fragments. Google has held that position for two decades. Chrome captures 65%...
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From Interface to Platform: What OpenAI’s Atlas Browser Might Really Signal

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.

Everyone saw this one coming: Open AI's Atlas browser. Rumors of an OpenAI browser had been circulating for months, alongside steady hints in partnerships, SDK updates, and app integrations. When Atlas finally arrived, it didn’t feel like a shock. The surprise isn’t that OpenAI built a browser. It’s why they built one, and what that might unlock. Because Atlas isn’t just another entry in the growing list of “AI browsers.” It’s the latest move in...
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Claude Skills Might Be Anthropic’s Most Exciting Update Yet

Claude Skills let users package reusable workflows that make Claude adaptable and modular: a practical leap in how AI assistants work.

I just tested Claude Skills, and it’s awesome. Anthropic is on a roll here with disruptive innovation. If you haven’t tried it yet, here’s a quick rundown of why it matters, and why it could reshape how we work with AI assistants. What Claude Skills are Skills are like snap-on capabilities for Claude. Instead of rewriting prompts or uploading instructions every time, you can package reusable logic, scripts, and guidelines into a small folder. Claude...
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AI Isn’t Replacing Human Help, It’s Redefining It

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 will be the greatest source of empowerment for all.” - OpenAI’s Fidji Simo It’s a bold vision, one that suggests anyone, anywhere, could find help for whatever they need: a business idea, a mental block, or even emotional support. The promise sounds inspiring, but it also sparks a familiar unease. We’ve heard versions of this before. Every major wave of technology begins with the same fear: that something deeply human will be lost. When...
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From 50 to 100: The Human Edge in an AI-Accelerated Product World

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 has changed the pace of product development. What once took months now takes weeks. We can ship prototypes in days, test them with users, and iterate instantly. The acceleration is real. But speed creates a new tension. If AI can take us from 50 to 90 in quality and execution, what does it take to reach 100? That final stretch, the space between something that works and something that resonates, is where human judgment...
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The New Rythm of Product, Design, and Engineering

AI-assisted prototyping is reshaping collaboration across product, design, and engineering—accelerating discovery and demanding the best from each discipline.

The lines between product, design, and engineering have always been fluid, but AI-assisted development is making that overlap more productive than ever. Today, product managers can spin up interactive prototypes in hours, not weeks. What used to require multiple handoffs between PMs, UX designers, and developers can now start as a shared experiment. This shift isn’t about replacing roles. It’s about accelerating discovery. Prototyping as a Discovery Tool There’s growing tension in some teams: product...
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Getting AI Right in Established Companies

Established companies can balance today’s business with tomorrow’s AI potential by sequencing AI-enabled and AI-native strategies.

Your product works. Customers rely on it. Revenue depends on it. Now everyone’s telling you to “go AI.” But what does that actually mean? Most established companies misunderstand the choice in front of them. They treat AI as binary. Either bolt on AI features to what they already have or tear it all down and start from scratch. Both approaches miss the real opportunity. The real strategy is knowing the difference between AI-enabled and AI-native...
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AI Agents Grow Work Instead of Replacing It

AI agents don’t replace jobs—they expand what teams can achieve. Learn how product leaders can turn automation gains into growth opportunities.

When a new technology shows up, most people ask, “Whose job will this replace?” A better question is, “What new work will this create?” In a recent interview with Every, Box CEO Aaron Levie shared a useful way to think about AI. He said AI agents don’t shrink human work. They expand it. By taking care of repetitive coordination, AI gives teams more room for creative thinking and faster experimentation. This idea matters for any...
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Rethinking Leadership Decisions through the Lens of Spotify’s Bets Board

Spotify’s “bets board” shows how leaders can treat decisions as experiments. Here’s how to explore that mindset without copying the model.

I came across something recently that caught my attention. Spotify’s executives have banned the words “offline” and “later” in leadership meetings. At first, it sounds like a linguistic tweak. But it connects to a deeper idea about how they make decisions — through what they call bets. Twice a year, Spotify’s senior leaders hold a “bet pitch” cycle. Each executive brings a small number of proposals backed by data and conviction. They pitch them to...
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Evals in AI Product Development

Evals make AI products measurable. Traces, annotations, and layered tests turn evaluation into a practical loop for reliability.

AI models don’t break like code, but they can drift, hallucinate, or mislead — which is why teams are turning to evals. The debate over whether every team needs them signals that we’re still learning how to measure quality in systems that learn on their own. What Evals Are For someone not familiar with evals, here’s a quick overview. Evals are structured tests that measure how well a model performs on real-world tasks. Unlike conventional...
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Planning Better with Claude Sonnet 4.5

Claude Sonnet 4.5 introduces usage tracking and thinking mode enhancements that streamline AI planning and session management.

I’ve been using Claude Sonnet 4.5 (in Claude Code) for a couple of weeks, and two small updates changed how I plan my sessions. The first is usage tracking. There’s now a simple command (/usage) that shows how much I’ve used Claude Code—both per session and across the week. It sounds minor, but it’s a big deal if you use Claude as a daily coding or research companion. Before this, I had no sense of...
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The Strategy-Outcome Connection: Moving Beyond the Feature Roadmap

How product managers can move beyond feature roadmaps to build strategy-driven outcomes, handle stakeholder pressure, and make sharper prioritization decisions.

The loudest voice problem If you’ve ever owned a roadmap, you’ve likely faced this. A senior leader walks into your review and says, “We need to build this feature next quarter.” The statement carries weight. It comes from experience, hierarchy, and often, conviction. You might even agree at first. Maybe you think, “Let’s build it once to gain trust.” Sometimes that’s a fair trade. But most times, that’s how the loudest voice in the room...
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The PM as Builder Era

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.

The best product managers I know are not writing more specs. They are writing code. AI is changing what it means to build, not by replacing PMs but by removing the constraints around what they can try. When the cost of testing an idea approaches zero, the right move is not to plan more. It is to prototype more. Today, you can build five versions of a concept before lunch. You can wire up a...
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ChatGPT Is Becoming the Interface

Sam Altman outlines how OpenAI plans to turn ChatGPT into the internet’s next interface, powered by apps, commerce, and global infrastructure growth.

When Sam Altman spoke with Stratechery this week, one idea stood out from the flurry of announcements and partnerships. OpenAI wants ChatGPT to be the single interface that connects people to everything else they do online. Altman described a clear vision. OpenAI aims to build one capable system that people can use across their entire lives, from work to learning to entertainment. That mission explains the company’s focus on three fronts: research, product, and infrastructure....
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Notes on the Modern Product Leader’s Playbook

Notes on strategy, speed, and why modern product leadership is a leverage game.

Watched Jiaona Zhang’s Reforge talk on product leadership. It’s a dense one — part philosophy, part tactical operating manual. These are the notes (and reactions) I don’t want to forget. We’re in an in-between moment where PMs are both strategists and builders again. Jiaona calls it a new playbook, but it’s really a reminder that our leverage has changed. Mindset: From Managing to Skating Where the Puck Is The core shift is from execution to...
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How I Scaled My Blog Archive with AI

How I built a scalable, AI-powered blog archive by learning GenAI coding constructs and focusing on product thinking, not syntax.

I’ve built this site from the ground up. Over the years, I’ve used nearly every blogging platform: WordPress, Ghost, Substack, and more. But with the rise of generative AI, I wanted to roll my own. No templates, no prebuilt themes. Just me, rolling up my sleeves and vibe coding every page and design element into existence. Part of this project is about learning firsthand how GenAI changes the way we build. I wanted to experience...
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OpenAI’s App Store Moment and the Future of Product Boundaries

OpenAI’s new ChatGPT app store redefines how users interact with products — shifting from interfaces to intent.

Yesterday, OpenAI launched its own app store — a full ecosystem for third-party apps that live inside ChatGPT. Spotify, Canva, Figma, Zillow, and Coursera are already in. At first glance, this might feel like another platform milestone. But if you zoom out, it’s something deeper: a redefinition of where products “live” and how users experience them. The interface is dissolving For years, we’ve built products around distinct interfaces — apps, dashboards, websites—each one with its...
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From Competitive Moats to Collaborative Bridges

In the AI era, the strongest products don’t build walls — they build bridges. Here’s why connectivity, not isolation, defines modern defensibility.

The AI ecosystem is moving too fast for moats. Every closed advantage leaks. Every walled garden gets mapped. What used to protect you now isolates you. The defensible position today isn’t the highest wall — it’s the bridge everyone else depends on to cross. For years, defensibility meant isolation. Own the data. Control the stack. Lock down the ecosystem. Those strategies worked when products were discrete and distribution was finite. You could draw boundaries around...
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The Feedback Loop Fallacy in AI Products

AI feedback loops can lie. Learn why engagement metrics fail and how product managers can rebuild truth-centered measurement systems.

For years, product managers have lived by a simple gospel: ship, measure, learn. The faster your feedback loop, the quicker your product improves. But AI is quietly breaking this law of motion. The feedback loops we’ve trusted for decades no longer tell the truth. When feedback starts lying In traditional software, user behavior is a reliable proxy for value. If conversion rates increase or churn decreases, the product has likely improved. With AI, that assumption...
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Platform Products Need to Earn Their Keep

Platform products need empathy and accountability. Treat them like external products — measure impact, earn trust, and prove real value.

Every company wants to build platforms. Few succeed. The promise sounds irresistible: build it once, reuse it across teams, and move faster forever. But inside most enterprises, “platform” has become a buzzword attached to sprawling systems that no one loves and everyone tolerates. Some of these platforms thrive because they are built with empathy and clarity. Others limp along as corporate mandates — used begrudgingly, updated reluctantly, and funded indefinitely. I’ve seen both ends of...
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Thinking Through Agentic Loops

Exploring how agentic loops extend feedback loops by adding autonomy, iteration, and goal-directed action in systems and AI.

I’ve long been fond of feedback loops. Systems thinking taught me to look for them everywhere: how a fitness tracker nudges you to walk more, how customer signals shape a product roadmap, how our habits form through repeated cues and responses. Feedback loops are elegant in their simplicity: an action produces an effect, which feeds back to influence the next action. Recently, I came across the phrase agentic loops. At first, it sounded like another...
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The Shift from SEO to AEO Is Redefining Visibility Online

The rise of answer engine optimization (AEO) marks a shift from SEO. Visibility in AI-driven answers is now the key to discovery.

When Reddit’s stock tumbled this week on concerns about traffic and AI exposure, headlines focused on the numbers. Stock prices fluctuate all the time. But the more interesting story is not Reddit’s market cap. It is the shifting landscape of how people and platforms connect to knowledge in the age of Answer Engine Optimization (AEO). >Promptwatch reportedly showed that on September 30, Reddit content was cited in just 2 % of ChatGPT responses — down...
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Sora 2 Changes the Video Play

Sora 2 pushes AI video into mainstream use. Here’s what it enables now, who gets disrupted, and why B2B teams should pay attention.

OpenAI’s Sora 2 is not just a model upgrade. It’s text-to-video with sound, physics that make sense, and a social app where anyone can remix clips. That shifts AI video from a lab demo to something that can spread in the wild. The following screenshot is from the video generated realistically with this prompt (shared by the Sora team): "A person is standing on 2 horses with legs spread. make it not slowmo also realistic....
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Adaptability, Creativity, Tech Fluency: The Skills Defining Work Now

The core skills once seen as future-ready—adaptability, creativity, and tech fluency—are already defining how work gets done today.

The World Economic Forum's Future of Jobs Report offers a clear signal for product managers, technologists, and business leaders: the skills that matter most in the coming decade are not the same as those that powered the past. Well, the report is confirming what we are already seeing in full force: By 2030, success will hinge less on manual or routine capabilities and far more on adaptability, creativity, and fluency in technology. !Core Skills 2030...
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AI Platforms as the New Distribution Layer

OpenAI’s Instant Checkout turns ChatGPT into a commerce channel. Here’s what product managers need to know about AI-native distribution.

Seven hundred million people use ChatGPT every week. That’s not just a user base, that’s a distribution channel that makes traditional retail look small. With its new Instant Checkout feature, OpenAI isn’t just adding payments. It’s signaling that AI platforms are on their way to becoming full-blown storefronts. For product strategists, this marks a shift as significant as the arrival of the App Store. Distribution itself is being rebuilt inside AI platforms. From Infrastructure to...
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The Factory Robot for Apps

Cloudflare’s VibeSDK acts like a factory robot for apps. It turns plain language into live software while teams stay focused on customer value.

Cloudflare announced a game-changing open source AI vibe-coding platform: VibeSDK. Think of it like a factory robot that understands plain language and builds the gadget you describe. You walk into a high-tech workshop and say, “I need a device that tracks expenses with clear charts.” The robot designs the blueprint, picks the parts, assembles the device, tests it, and rolls out a working demo in minutes. You request a tweak, and it updates the device...
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Level Up or Get Left Behind by AI

Walmart and Accenture show why AI is an existential risk for workers who don’t adapt, but also a chance to reinvent work for the better.

The sugarcoating is over. Walmart’s CEO Doug McMillon says, “AI is going to change literally every job.” Accenture’s CEO Julie Sweet is blunt too — some employees will be retrained, others will be exited. The world’s biggest employers are making it clear: if workers don’t adapt, they risk being left behind. The Existential Risk The risk is not just about losing jobs, but about jobs losing relevance. At Walmart, warehouse automation is already cutting some...
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Rethinking Product, Market, Channel, and Model for AI Era

How Brian Balfour’s Four Fits framework has been updated for the AI era, and what product leaders can learn from the shift.

Frameworks that endure disruption are rare. Brian Balfour’s original Four Fits framework has long been a foundational lens for growth strategy. He recently released The Four Fits: A Growth Framework for the AI Era to capture how AI is shifting the constraints inside each dimension. The Four Fits have always been about scaling companies to $100M+ revenue at venture speed. To succeed, all four fits must align simultaneously. In this article, I explore the evolution...
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From Architect to Gardener to Orchestrator in the AI Era

How AI transforms product leadership from building to conducting. The rise of the Orchestrator mindset in product management.

Last year, I wrote about two product management mindsets: the Architect who blueprints everything upfront, and the Gardener who plants seeds and discovers what grows. That framework made sense when humans did all the work. Not anymore (or not very soon). AI is changing the game. It can architect better than architects (generating requirements, writing specs, and creating test cases). It can garden better than gardeners (running thousands of experiments, adapting in real-time, finding patterns...
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What a Gigawatt of AI Really Means

What a gigawatt of AI really means, and how abundant intelligence could reshape technology, healthcare, and society.

Sam Altman wrote yesterday about a future of abundant intelligence, imagining a world where we add a gigawatt of new AI infrastructure every week. This week, we already saw their partnerships with Nvidia and Oracle. He teased about more partnerships and details coming soon: >"If AI stays on the trajectory that we think it will, then amazing things will be possible. Maybe with 10 gigawatts of compute, AI can figure out how to cure cancer....
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From Product-Led to Product-Agentic Growth in B2B

Product-Agentic Growth reframes how B2B software must evolve: AI agents doing work for users, reshaping PLG with autonomous workflows, predictive expansion, and real ROI.

Picture this: A procurement manager signs up for your B2B marketplace. Within 30 minutes, your product has analyzed their company's spending patterns, identified $2M in potential savings, and pre-vetted 15 suppliers that match their compliance requirements. It drafted three RFPs based on their historical templates and scheduled demos with the right stakeholders. The procurement manager didn't do any of this. The product did. This isn't just good onboarding. It's not even personalization in the traditional...
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When Work Becomes the Practice

Moving beyond the search for meaning to the practice of creating it. A product manager's reflection on making work matter, one sprint at a time.

A colleague and an inspiring leader, Puneet Maheshwari, recently wrote something about work and meaning that stopped me in my tracks. He talked about growing up around people who never had the luxury of romanticizing "meaning" in work. For them, work was survival and dignity. Nothing more, nothing less. His insight? The question isn't whether work is a means to an end, but which ends make the means worth it. When Time Disappears For me,...
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The Massive AI Opportunity Hiding on Your Home Screen

Most 'AI products' aren't AI-native. Use the Home Screen Test to spot the billion-dollar opportunities hiding in plain sight.

Right now, stop reading and look at your phone's home screen. Count how many apps are built specifically for AI—not regular apps that added AI features, but products designed from the ground up for the AI era. ChatGPT probably makes the list. Maybe a few others. But for most of us, the answer is surprisingly close to zero. This observation comes from Andrew Chen's recent piece on how AI will change startup building, where he...
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Platform vs Product: The AI Era Convergence

AI is collapsing the line between platforms and products. The winners will master both, balancing ecosystems and user experiences.

“In technology, whoever controls the platform controls the narrative,” as several strategic analysts have observed. The rise of AI is testing that maxim in new ways. A single large language model can be both the underlying platform that developers build on and the end-user product millions adopt directly. For companies in the AI era, the question is no longer whether to be a platform or a product, but how to navigate being both at once....
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Atlassian's Browser Move

Atlassian’s $610M bet on The Browser Company is bold. Here’s why it makes sense, and the big risks that could derail it.

Atlassian, the company behind Jira and Confluence, is spending $610 million to acquire The Browser Company, the maker of Arc and the newer AI-forward browser, Dia. That sounds strange at first. Atlassian makes collaboration software, not browsers. Chrome and Edge dominate the market. Why on earth would they want to own a browser? But once you look closer, it starts to make sense. The browser as a starting point Brian Balfour puts it well in...
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When to Trust Intuition vs. Metrics

Intuition is a compass, metrics are a map. Here’s how product managers can decide which to trust, depending on the product stage.

This is a follow-up from an earlier post on the limit of metrics. Product managers often wrestle with a familiar question: Should I trust the numbers, or should I trust my instincts? The truth is, both matter — but their weight changes depending on where your product is in its lifecycle. Intuition plays a bigger role early, while metrics take over later. Knowing when to lean on which can be the difference between chasing noise...
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AEO is the New SEO?

A quick look at Answer Engine Optimization (AEO), why it matters for both consumers and businesses, and how it differs from SEO.

Most product and marketing teams already know SEO. Search engine optimization has been the backbone of digital visibility for decades. But a new acronym is creeping into conversations: AEO, or Answer Engine Optimization. I’m still digging into it, but here’s what I’ve learned so far—and why it matters. From Search Engines to Answer Engines SEO is about ranking high in search engine results. When a buyer types a question into Google, the goal is to...
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Apple’s Sugar Water Trap

Apple’s iPhone 17 shows the sugar water trap risk as AI reshapes tech. A lesson for product managers on balancing incremental progress with bold bets.

Steve Jobs once asked John Sculley, “Do you want to sell sugar water for the rest of your life or come with me and change the world?” That question pushed Sculley to leave Pepsi for Apple, and it has lingered ever since as a reminder of the difference between comfortable success and transformative ambition. The launch of the iPhone 17 makes the metaphor newly relevant. On paper, Apple delivered a strong upgrade: a Promotion display...
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Learning How to Learn Is Your Real Superpower

Learning how to learn is the real superpower for product managers. It’s not about speed, but reinvention, adaptability, and enjoying the process.

“It’s very hard to predict the future, like 10 years from now, in normal cases. It’s even harder today, given how fast AI is changing, even week by week. The only thing you can say for certain is that huge change is coming.” Demis Hassabis, speaking at the Odeon of Herodes Atticus Demis Hassabis, CEO of Google DeepMind, in the same context, said the most important skill of the future isn’t coding, design, or even...
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The Hidden Cost of UX Friction in Enterprise Systems

Enterprise systems often rely on mandates, not UX. But small friction compounds into real business risk. Here’s why PMs can’t ignore it.

Following up on my earlier piece: Build, Buy, or AI-Build, in which I noted Marty Cagan's view that AI will not easily replace enterprise solutions, even in the age of “vibe coding.” His reasoning is sound: enterprise products are deeply embedded in intricate workflows, with business rules and integrations that can’t be swapped out overnight. Today, tools like Copilot or low-code builders tend to play a helper role rather than a wholesale replacement. But this...
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Claude Now Builds Spreadsheets and Documents

Claude now generates real files—Excel, Word, PowerPoint, and PDFs—from prompts.

Claude just got a big upgrade. According to Anthropic’s announcement, it can now create real files: Excel spreadsheets, Word documents, PowerPoint decks, even PDFs—straight from your prompts (whether you're working in Claude.ai or the desktop app). Here's how Anthropic puts it: Turn data into insights: Give Claude raw data and get back polished outputs with cleaned data, statistical analysis, charts, and written insights explaining what matters. Build spreadsheets: Describe what you need—financial models with scenario...
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One Question Every Product Manager Should Ask in Roadmap Reviews

A simple question can sharpen roadmap reviews: what will this feature replace in the user’s life? Here’s why the replacement lens matters.

Roadmap reviews tend to focus on timelines, dependencies, and long lists of features. These discussions are important, but they often miss a single clarifying question that can cut through the noise: What will this feature replace in the user’s life? Asking this question changes the framing. Instead of thinking about what a feature adds, the conversation shifts to what it displaces. Users don’t have unlimited time or attention. Every new feature competes with something they...
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What Leadership Really Looks Like

Leadership for product managers isn’t about titles. It’s about daily choices—small acts of influence, initiative, empowerment, and courage.

In the product culture series, I want to delve into who the leader is. In corporate life, “leader” is a word that gets stretched in too many directions. Sometimes it refers to someone with direct reports. Sometimes it points only to the highest rung of the ladder. But the truth is simpler: leadership is not about job level or headcount. Leadership is about how you show up. It’s about whether you create momentum, clarity, and...
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GTM Playbook for Feature Products in the Platform and AI Era

A GTM playbook for feature products competing with platforms in the AI era, focused on delight, speed, and switching costs before the bundle arrives.

Clubhouse and Twitter Spaces. Zoom and Microsoft Teams. Dropbox and Google Drive. The pattern is not about who shipped first or who had the clever feature. The pattern is that platforms with native distribution absorb features, then win on adoption. In 2025, AI accelerates that cycle. Features can be cloned in months, not years, and updates land on millions of seats overnight. This is not a reason to stop innovating. It is a call to...
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APIs are the Strategic Foundation for Agentic AI and Beyond

APIs are not small. They are the backbone of digital growth and the foundation of agentic AI and MCP orchestration.

(Expanding on my earlier quick-thought piece on APIs) APIs Hidden in Plain Sight APIs are often dismissed as “technical plumbing,” invisible to most business leaders. Yet they quietly power nearly every digital interaction, from mobile payments to streaming recommendations. Some of the most valuable companies in the world—Amazon, Stripe, Twilio—built their fortunes by turning APIs into products. Now, APIs are entering an even more strategic chapter. They are becoming the backbone of agentic AI and...
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The Token Squeeze is Real

AI isn’t getting cheaper. Token demand is exploding, and flat-rate subscriptions are doomed. What pricing models can survive the squeeze?

AI should feel like it's getting cheaper. After all, compute costs fall, models get optimized, and every year brings new claims of a 10x drop in inference prices. But as Ethan Ding argues in Tokens Are Getting More Expensive, the opposite is true: the economics of AI subscriptions are in a squeeze. Ding’s Core Argument The paradox is simple. While yesterday’s models do get cheaper, users don’t want them. Demand instantly shifts to the latest...
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When AI Bots Rule the Web

AI crawlers dominate web traffic, but most don’t send users back. Here’s what product managers need to know about training bots, referrals, and strategy.

Most of the traffic hitting websites today is no longer human. Cloudflare’s AI Insights dashboard makes this clear: the majority of crawling comes from AI bots, and the balance of power among those bots is shifting fast. For product managers, that reality changes how we think about traffic, attribution, and strategy. !AI Insights Cloudflare Training bots dominate Close to 80% of AI crawler traffic serves training purposes. These bots pull content to feed large language...
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Nano Banana and the Future of AI Image Editing

Google’s Nano Banana is redefining AI image editing. Here’s what it means for creativity, platforms, and trust in the digital age.

When Google teased three bananas in a post from CEO Sundar Pichai, the internet buzzed with curiosity. The reveal—Nano Banana (aka Gemini 2.5 Flash Image), a new AI image editing model. It was more than a quirky codename. It signals a shift in how we think about digital creativity. Unlike earlier AI tools that struggled to maintain consistency or required heavy post-editing, Nano Banana delivers precise, natural-language edits while keeping subjects unmistakably themselves. This is...
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What Makes a Real Data Moat

Data moats are the key to AI defensibility. Tesla and Stripe show what makes them real and how product teams can build them.

The age of generative AI has created a strange paradox. On one hand, anyone can plug into models like GPT and build features quickly. On the other hand, defensibility has never been more elusive. If everyone has access to the same foundation models, what stops a competitor from copying your product? The strongest answer is the data moat. Done right, it’s the most durable form of AI advantage a company can build. Done wrong, it’s...
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AI in Product Management

AI is moving fast in product management, from PRDs to prototypes. Here’s what research shows, what’s missing, and how PMs can lead.

AI in product management is no longer a question of if. It is a when. And when we say 'when,' we are not talking about years. We are talking months, given the pace of innovation and adoption. A new study in Management Review Quarterly, “Where does AI play a major role in the new product development and product management process?” by Aron Witkowski and Andrzej Wodecki, maps out the current state of AI in product...
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Agentic Browsers Meet Their Hardest Test: Security

Agentic browsers face significant security risks, such as prompt injection, but early defenses demonstrate why security will be the true differentiator.

Claude for Chrome (now in pilot), Perplexity’s Comet, and Dia are all pushing the idea of a browser that doesn’t just display pages but acts within them. But as soon as you let an AI click, type, and execute, the hardest problem comes into view: security. The quiet threat of prompt injection Anthropic deserves credit for going deep on vulnerabilities in its Claude for Chrome pilot. “Some vulnerabilities remain to be fixed before we can...
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The Big Squeeze in B2B and the Challenge of Lasting Defensibility

In B2B, escape velocity isn’t enough. Startups must turn rapid distribution into lasting defensibility before incumbents close the window.

AI has created the fastest-scaling companies we’ve ever seen. Lovable, for instance, hit $100 million ARR just eight months after launch. As Brian Balfour observes in The Big Squeeze, “Escape velocity elevated Lovable from obscurity to household name. And now the company has a real chance to build a large and successful business. But there’s no guarantee they’ve found long-term defensibility or can turn this wave of interest into a sustainable business.” That tension—between speed...
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Agentic AI Needs APIs to Act

Agentic AI can reason, but it needs APIs to act. APIs are the execution layer that makes AI autonomy real.

APIs are often seen as back-office plumbing, but in the emerging world of agentic AI, they are the execution layer that makes autonomy possible. Without APIs, AI remains stranded in theory—able to reason, but unable to act. From Copilots to Agents The last wave of AI adoption has been copilots—tools that help users write emails, summarize documents, or draft code. These copilots assist, but they don’t take initiative. Agentic AI is different. Agents can plan,...
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No New Ideas in AI? The Power and Limits of Data

A critique of the idea that AI progress is only about data, exploring the role of algorithms and human creativity alongside new datasets.

The claim that there are no new ideas in AI, only new datasets, is both provocative and partly true. As Jack Morris argued in his recent post, many of the most important AI milestones have been driven not by theoretical leaps, but by new sources of data. He puts it succinctly: “The breakthroughs weren’t big ideas; they were new ways to learn from new kinds of data.” from blog.jxmo.io That framing resonates with history. The...
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Why Customer Success Belongs at the Start of Product Strategy

Shift-left Customer Success by embedding it early in product strategy, design, and GTM to boost retention and drive SaaS growth.

Customer Success (CS) is one of the most misunderstood roles in SaaS. As Saahil Karkera wrote in a widely shared LinkedIn post, one quarter CS teams are heroes; the next, they're blamed for churn, adoption drops, and burnout. This volatility exists because CS sits at the fault lines of Product, Sales, and Customer expectations. The solution isn’t hiring “miracle CSMs.” It’s treating Customer Success as a shift-left strategy—designed into product, GTM, and organizational incentives, not...
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Observability Now Includes Watching AI

AI observability means monitoring accuracy, drift, and hallucinations, not just uptime. PMs must treat it as a core product feature.

When product managers think of observability, they usually mean uptime, latency, or error rates. But as AI becomes central to user experiences, that definition must expand. Observability now includes monitoring model accuracy, hallucinations, prompt injection, and real-time behavior. As Datadog’s CPO Yanbing Li notes, AI systems add a new layer of complexity to enterprise monitoring. Why AI demands a new observability lens Traditional software is deterministic. If a server or a function fails, you can...
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Fixing Google SEO Indexing Issues with ClaudeCode

Fixed Google SEO indexing issues using ClaudeCode by adding canonicals, updating the sitemap, and cleaning redirects—no SEO expertise required.

Most SEO problems look scarier in Google Search Console than they really are. Recently, I ran into one of those situations. Google flagged 17 indexing issues across my site: 16 pages marked as “Page with redirect” 1 page flagged as “Duplicate without user-selected canonical” At first glance, this looked like something I’d need SEO expertise to fix. But a quick debugging session with ClaudeCode showed me it was manageable with a bit of structured troubleshooting....
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Why Empathy, Not IQ, Defines Success in the AI Age

Empathy and critical thinking—not IQ—are the keys to thriving as a product leader in the AI era.

Walk into any workplace today, and you’ll see AI embedded in daily tools and workflows. It drafts emails, generates reports, and even proposes design ideas. What it can’t do is sit across from someone, understand their frustration, and respond with care. That distinctly human capacity is becoming the true differentiator. Carnegie Mellon professor Po-Shen Loh puts it bluntly (video): “The only sustainable trait in the age of AI is the ability to care about people...
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Build, Buy, or AI-Build

Vibe-coding opens a new AI-build path, but Marty Cagan’s point on business rules shows its limits. Can AI ever capture this hidden and complex logic?

In my recent post on build vs buy in the age of vibe-coding, I argued that the classic binary is breaking down. Thanks to generative AI tools, teams now face a third option: AI-build. Instead of waiting for engineering capacity or relying entirely on vendors, product managers can prototype, test, and even wire together solutions themselves using natural language. Marty Cagan just published a piece on build vs buy in the age of AI. He...
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Vibe-Coding Is Early But Already Changing SaaS

Vibe-coding is still early, but already empowers non-technical builders while pressuring SaaS vendors to deliver leverage beyond features.

In a recent Every article, Dan Shipper highlights people who replaced expensive SaaS tools with AI-built alternatives. The stories aren’t just about cost-cutting. They show how quickly software creation is becoming accessible to people who never considered themselves builders. This is still early days. Vibe-coding — natural language prompting to generate working tools — is in the first phase of its maturity curve. It often takes a few iterations to get things right, as Shipper’s...
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How Product Leaders Can Adapt and Thrive in the AI Era

Practical playbooks for product leaders to adapt and thrive in the AI era using wedge expansion, jobs-to-be-done, and dual transformation.

In the first post of this series, we looked at why AI disruption affects startups, giants, and companies with product-market fit differently. We saw that structural forces—like scale economies, network effects, and capability stacks—shape who adapts and who stalls. This post turns from why to how. The real challenge for product leaders is not predicting disruption but navigating it. While AI is reshaping every industry, companies that apply structured playbooks are better positioned to adapt...
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The Informal Committees Behind B2B Buying

B2B buying isn’t decided by end users alone. Informal committees shape decisions, and product managers must map their jobs-to-be-done.

When we think about product adoption, the focus usually falls on the end user. Product managers map user needs with frameworks like jobs-to-be-done (JTBD), ensuring the product fits a real workflow. But in B2B, adoption doesn't always equal purchase. Deals often hinge on an informal buying committee — a shifting group of individuals who influence or approve decisions, even if they never use the product directly. This isn’t a boardroom-style committee. It’s a loose network...
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Why Startups Struggle and Giants Stall in the AI Era

Why AI disruption challenges startups and giants while firms with product market fit adapt faster, explained through proven strategy frameworks.

Sam Altman has observed that both startups and large companies face unique struggles during the current wave of AI disruption, while firms that already have product-market fit often adapt more effectively (OfficeChai). Startups, despite their speed, often lack the foundation to scale. Giants, despite their resources, get trapped in bureaucracy. Companies with strong user adoption and proven fit, on the other hand, can use AI to deepen their advantage. This raises an important question for...
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Making Product Decisions with a Bets Mindset

How leading product teams use betting principles to make smarter decisions, test ideas fast, and adapt quickly to real-world results.

When you build products, you’re making bets — not certainties. The best product teams don’t pretend to know the answer or wait until all data clears the fog. Instead, they “think in bets.” That means approaching each decision like a poker player, not a chess grandmaster. Most people treat product roadmaps as if they’re a set of sure things: follow steps A, B, and C, and you’ll win. But real product work faces incomplete data...
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Start with Product and Target for Effective Distribution, Not Channel

Learn why smart product managers match channels to product and target, not trends, with a simple hospital software example.

When it comes to getting your product into the hands of customers, many new product managers start with the channel. They ask, “Should we sell through partners, go viral, or build a sales team?” Ben Horowitz puts it simply: “A properly designed sales channel is a function of the product that you have built and the target … that you wish to pursue.” In other words, the product and the target market come first. The...
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AI Risks to SaaS Companies

Generative + Agentic AI is accelerating feature commoditization; read the room and adapt.

The press around AI putting pressure on well-established SaaS companies is gaining some momentum. Note: We are not discussing specific stocks and valuations. Our focus is on the impact of AI on software companies. Analyst Ratings Published 08/11/2025… "Melius Research downgraded Adobe… warns of ongoing multiple compression for software-as-a-service companies… ‘AI is eating software’ …” AI isn’t a shiny add-on anymore. It’s like a sneaky wave that’s pushing SaaS valuations lower. Investors see that and...
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Product Managers Who Think in Systems Will Survive the AI Era

AI is reshaping product management. Learn how to map, optimize, and automate your systems before someone else does it for you.

AI is not a side trend. It is changing the work of product managers right now. Elena Verna wrote about eliminating her own job in Growth by automating 101. Read it in full. Her point was simple. If you automate yourself, you survive. If you do not, you are replaced. She was right on. But for product managers, this is not only about using a few AI tools. It is about thinking in systems. Your...
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The Power of an Anchor - Warby Parker's Pricing Strategy

Warby Parker's playbook for product managers. Learn how the company uses pricing strategy, vertical integration, and innovation to grow.

From WSJ piece on Warby Parker: >Many things have gotten pricier in the past 15 years. Not Warby Parker's most affordable glasses, which have cost $95 since the brand’s inception in 2010. Warby Parker grew 14% last year. It did this while keeping its hero $95 price point. This shows that a focused value proposition can thrive even with inflation. The company used a few key strategies. It controlled its supply chain. It created a...
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From Promise to Practice - AI's Real Impact on Medicine

Real-world data on AI in medicine. Where it works (imaging, drug discovery), why it fails (data, integration), and what actually drives adoption.

Yesterday, we explored how AI transforms medical understanding, informing patients. Today, let's examine where AI actually delivers results in clinical practice. New research from the Journal of Clinical Medicine maps the gap between hype and reality. Resonates well with my personal experience. >"The central challenge is evident: as AI tools become more sophisticated, our capacity to integrate them ethically, equitably, and effectively into clinical practice must evolve in tandem. This editorial explores the remarkable progress...
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When AI Becomes Your Medical Translator

How AI LLMs are transforming healthcare by empowering patients with PhD-level medical knowledge and reshaping the entire industry ecosystem.

Picture this: You receive an email from your doctor with three different cancer diagnoses. Your heart stops. The medical jargon feels like it's written in a foreign language. But instead of spiraling into a Google rabbit hole of worst-case scenarios, you take a screenshot and upload it to ChatGPT. Within seconds, you have a clear, understandable explanation of what you're facing. This isn't a hypothetical scenario—it's exactly what happened to Carolina, one of the patients...
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The Winner's Curse: Rhyming History in the AI Era

Why AI disruption differs from past paradigm shifts: faster cycles, probabilistic computing, and why today's tech winners face an accelerating curse.

Ben Thompson's latest piece hits on something crucial: when computing paradigms shift, yesterday's winners often become tomorrow's strugglers. >The risk both companies are taking is the implicit assumption that AI is not a paradigm shift like mobile was. In Apple’s case, they assume that users want an iPhone first, and will ultimately be satisfied with good-enough local AI; in AWS’s case, they assume that AI is just another primitive like compute or storage that enterprises...
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How to Make OKRs Work

Practical tips to make OKRs work: writing strong objectives, measurable key results, and avoiding common pitfalls in execution.

This is Part 2 of a two-part series on OKRs inspired by John Doerr’s book Measuring What Matters. You can read Part 1 here: Why OKRs Matter. OKRs are simple to understand, but deceptively hard to get right. Many teams write OKRs once, post them in a slide deck, and never look back. Others confuse them with KPIs or use them as a laundry list of tasks. The result is disappointment: OKRs become busywork rather...
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Love the Problem, Solution will Follow

Fall in love with the problem, not the solution. Learn seven techniques to uncover customer needs and build products that create lasting impact.

When you’re building a product, it’s easy to get excited about the “how.” The sleek design, the advanced tech stack, the long feature list. But here’s the hard truth that separates great products from forgettable ones: don’t fall in love with the solution; fall in love with the problem. This mindset shift can be the difference between a product that thrives and one that just exists. Albert Einstein once said, “If I had an hour...
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Why OKRs Matter

Learn why OKRs matter, how they align teams, and the four superpowers that make them a proven framework for execution.

This is Part 1 of a two-part series on OKRs inspired by John Doerr’s book Measuring What Matters. In Part 2, we’ll explore how to make OKRs work in practice. Most organizations don’t fail because of a lack of effort. They fail because energy is scattered across too many priorities. Objectives and Key Results, or OKRs, provide a way to channel focus toward what truly matters. An OKR has two parts: Objective: a clear, inspiring...
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Beyond the Deliverable - The Strategic Product Mindset

Learn how to move beyond delivery and adopt a strategic product mindset with practical steps, proven frameworks, and customer-first thinking.

"Strategic thinking." Sounds lofty, doesn’t it? The kind of thing we expect from leaders, not just order-takers. But here’s the truth: it’s not an inborn talent. It’s a skill that can be developed with deliberate practice. Like learning to ride a bike, or in our world, learning to ship something that truly matters. Too often, what gets labeled as “strategy” is really just a diagnosis or a broad policy statement. What’s missing are the coherent...
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Making Better Product Decisions

Great product leaders know not all decisions are equal. Learn how to apply the one-way vs. two-way door lens to improve decision speed and quality.

Great product leaders aren’t defined by their roadmaps, but by the decisions that shape them. Roadmaps shift. Markets change. But decision quality compounds over time. One useful lens comes from Jeff Bezos: the idea of one-way vs. two-way doors. One-way doors are irreversible. Once you step through, it’s costly to turn back. These require deliberation, diverse perspectives, and often leadership involvement. Two-way doors are reversible. If the decision doesn’t work out, you can step back...
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Jobs-to-be-Done - Demand Reducers and Systems Thinking

Explore the demand reducers in Jobs-to-be-Done—Inertia and Anxiety—and how systems thinking helps overcome hidden barriers to product adoption.

In Part 1, we explored the forces that generate demand: the push of dissatisfaction with the status quo and the pull of a better future. Together, they explain why customers look for change and what attracts them to a solution. But even when push and pull are strong, adoption isn’t guaranteed. Hidden forces often prevent products from being hired. These are the demand reducers: Inertia and Anxiety. As Alan Klement describes, these forces are as...
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Jobs-to-be-Done and the Forces that Create Product Demand

Learn how Jobs-to-be-Done explains the forces that create product demand—Push and Pull—and why progress, not features, drives adoption.

We hear a lot about being “customer-centric.” It’s on slides, in strategy decks, and peppered into pitches. But too often it’s a buzzword. The real test is this: do we truly understand why customers choose our products—or why they don’t? The Jobs-to-be-Done (JTBD) framework, shaped by thinkers like Alan Klement, offers a clearer lens. Customers don’t buy products because of features alone. They “hire” them to make progress in their lives. That progress is the...
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The Questions Great Product Leaders Ask

Great product leaders don’t rely on perfect foresight. They ask sharper questions that cut through ambiguity and lead to better decisions.

Great leaders aren’t the ones with all the answers. They’re the ones who know which questions matter. Nowhere is this truer than in product decision-making. When facing ambiguity, strong product leaders resist the urge to rush into solutions. Instead, they slow down just enough to ask sharper questions that cut through noise. A few that consistently elevate decision quality: Do we have the expertise to make this decision? If not, who needs to be in...
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