Product Management Insights

The New York Times profiled a start-up with 28 employees serving nearly 50 million users. That company is us. The traditional startup playbook: raise massive funding, hire hundreds of employees, and worry about profitability "later." But there's another way. Everyone at Gamma could fit in a small restaurant. We're not just surviving—we've been profitable for 15+ consecutive months, with revenue growing month over month, and lifetime negative net burn (we have more money in the bank than we've raised). This isn't an accident. We've deliberately designed our organization to maximize impact per person. Instead of creating specialist silos, we hire versatile generalists who can solve problems across domains. Rather than building management hierarchies, we find player-coaches who both lead and execute. Our team leverages AI tools throughout our workflow - Claude for data analysis, Cursor for coding efficiency, NotebookLM for customer research synthesis. These aren't just productivity hacks; they're force multipliers. Examples: — When our growth PM needed better analytics, he didn't file a ticket with a data team—he built a self-serve system that anyone can use without SQL knowledge. — When our marketing lead needed to understand our customers better, she fed thousands of interactions into an LLM and created actionable personas that now guide our entire strategy. — When our design team needs to test a hypothesis, we create a rapid prototype and show it to our power users. What we're seeing isn't just about "doing more with less." It's about fundamentally changing what's possible per person. The most valuable employees aren't specialists who excel in narrow domains - they're resourceful problem-solvers who continuously expand their capabilities. This approach creates remarkable resilience. Since everyone understands multiple functions, we don't have single points of failure when someone leaves or moves to another project. If you're building today, the question isn't how quickly you can scale headcount … it's how much impact you can create with the smallest possible team. The future belongs to tiny teams of extraordinary people.

AI Product Management AI Product Management is evolving rapidly. The growth of generative AI and AI-based developer tools has created numerous opportunities to build AI applications. This is making it possible to build new kinds of things, which in turn is driving shifts in best practices in product management — the discipline of defining what to build to serve users — because what is possible to build has shifted. In this post, I’ll share some best practices I have noticed. Use concrete examples to specify AI products. Starting with a concrete idea helps teams gain speed. If a product manager (PM) proposes to build “a chatbot to answer banking inquiries that relate to user accounts,” this is a vague specification that leaves much to the imagination. For instance, should the chatbot answer questions only about account balances or also about interest rates, processes for initiating a wire transfer, and so on? But if the PM writes out a number (say, between 10 and 50) of concrete examples of conversations they’d like a chatbot to execute, the scope of their proposal becomes much clearer. Just as a machine learning algorithm needs training examples to learn from, an AI product development team needs concrete examples of what we want an AI system to do. In other words, the data is your PRD (product requirements document)! In a similar vein, if someone requests “a vision system to detect pedestrians outside our store,” it’s hard for a developer to understand the boundary conditions. Is the system expected to work at night? What is the range of permissible camera angles? Is it expected to detect pedestrians who appear in the image even though they’re 100m away? But if the PM collects a handful of pictures and annotates them with the desired output, the meaning of “detect pedestrians” becomes concrete. An engineer can assess if the specification is technically feasible and if so, build toward it. Initially, the data might be obtained via a one-off, scrappy process, such as the PM walking around taking pictures and annotating them. Eventually, the data mix will shift to real-word data collected by a system running in production. Using examples (such as inputs and desired outputs) to specify a product has been helpful for many years, but the explosion of possible AI applications is creating a need for more product managers to learn this practice. Assess technical feasibility of LLM-based applications by prompting. When a PM scopes out a potential AI application, whether the application can actually be built — that is, its technical feasibility — is a key criterion in deciding what to do next. For many ideas for LLM-based applications, it’s increasingly possible for a PM, who might not be a software engineer, to try prompting — or write just small amounts of code — to get an initial sense of feasibility. [Reached length limit. Full text: https://lnkd.in/gYY-hvHh ]

State of the product job market: 1. There’s indeed a shift to hiring more senior PMs 2. Remote PM jobs are shrinking fast (down 35% from peak, and still shrinking) 3. More than one in five open PM roles is based in the San Francisco Bay Area (and still growing) 4. Bengaluru, India, is now the most popular location outside the U.S. for PM roles 5. The three most common hard skills that hiring managers are looking for from PMs are SQL, Jira, and experience with LLMs 6. The fastest-shrinking roles right now are diversity and scrum masters 7. Machine-learning engineers and data engineers are the most in-demand tech functions right now This analysis is based on exclusive data from two companies that I’m partnering with: 1. TrueUp, which tracks new job openings across all tech companies globally 2. Live Data Technologies, which tracks job changes (hires, departures, and promotions) across all companies in the U.S. Combining these two data sources reveals a whole new perspective on what’s really happening in the job market. Loads more surprises and insights in the full post: https://lnkd.in/gjArRAXm I’m going to continue tracking job market data and sharing regular updates on what’s changing in the product hiring market every few months or so. If there’s any data or questions you want us to dig into, please let me know in the comments.

New product initiatives within large companies often fail to achieve their potential because they have too much rather than too little. They have too much: 1) Headcount You are now under pressure to come up with something for all these people to do. Especially in cultures where “engineers must always be coding” and a PM is seen as failing if engineers are even briefly “blocked on requirements.” 2) Democratic decision making Creative ideas get killed (or watered down) by groups — yet this is the default in most big companies, even those that claim to use RAPID or similar frameworks. 3) Optics requirements You must now manufacture metrics and milestones to show straight-line progress and demonstrate certainty — during what is, by its very nature, an uncertain journey. 4) Involvement of the “core” product group To appease the leaders of the company’s cash cow, you make compromises that weaken your product. These leaders have the most power within the company and some may even try to confuse the CEO or quietly sabotage your initiative. 5) Reliance on the company’s distribution Due to the mirage of distribution, you won’t be incentivized to deeply understand your customer like a real startup would. Your initial traction is misleading — you get a usage spike, but: (a) those users are scattered across segments, not your core segment (have you even identified that core segment?) (b) what’s given will be taken away — that homepage slot for your new product will disappear next quarter due to VP jealousy or shifting OKRs (with some hand-wavy “metrics neutral” excuse). So if you are leading a new initiative within a larger company and your CEO/CxO asks you what you need to succeed, do not default to the answer that everyone in this situation gives: “I need more resources”. Instead, consider asking for less — less reporting, less certainty, less consensus-driven decision making, less meddling, and less pressure to build out a “full team” & great operations early on. If your CEO is competent, they’ll respect it. (clearly, this entire post is only for the intrepid product leaders who want to make winning products, it is not for everyone 🙂)

Confusing "product owner" with "product manager" is like confusing "Scrum Master" with "engineering leader." Product Owner is a role in Scrum. Product Manager is a career. This distinction matters more than most companies realize. When you only recognize Product Owner roles, you're signaling that product work is just about managing backlogs and running ceremonies. That's tactical execution, not strategic leadership. The actual career path looks like this: Associate PM → PM → Senior PM → Director → VP → CPO. And as you progress, your work fundamentally shifts. Early in your career, you might spend 70% of your time on tactical work, like writing requirements, managing stakeholders, coordinating releases. But as you advance, that flips. Senior product leaders spend 70% of their time on strategy, vision, market positioning, and organizational alignment. Companies that don't create proper career paths for product managers struggle to attract and retain top talent. Why would an experienced PM take a "Product Owner" role when they're ready to drive strategy at the director level? If you want to build a strong product organization, start by recognizing product management as the strategic function it is, not just a project coordination role. On podcast episode 252 of the Product Thinking with Melissa Perri, I explore the difference between Project and Product management, which have also created many misconceptions in the industry. Attached you can see the visual from my book “Escaping the Build Trap” from 2018. How well does this model still hold up in 2026?

𝗣𝗲𝗿𝗳𝗲𝗰𝘁 𝗼𝗻 𝗽𝗮𝗽𝗲𝗿. 𝗗𝗲𝗮𝗱 𝗶𝗻 𝗿𝗲𝗮𝗹𝗶𝘁𝘆. 𝗜𝗻 𝗘𝗣𝗖 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀, 𝗼𝗻𝗹𝘆 𝗼𝗻𝗲 𝘁𝗵𝗶𝗻𝗴 𝗰𝗼𝘂𝗻𝘁𝘀: Built –> on time, within budget, at the required quality. But here’s what happens instead: - In engineering, people try to squeeze out the last kWh, even if it delays construction. - In procurement, they negotiate for two more cents, and miss the ordering window. - On site, everything comes to a halt because a mounting hole pattern doesn’t fit. Instead of drilling, they call the supplier for three days. Seriously? Why? Because everyone’s thinking inside their bubble. Their silo. Their scope. Their personal KPIs. But an EPC project is not a theoretical exercise –> it’s execution under pressure. As an EPC project manager (or above), you don’t have the luxury of perfectionism. You need the full picture, and the courage to say: Enough optimizing. Let’s build. Because in reality, over-optimization often means: - Perfect calculations on paper. - Grid connection slot missed. - Project not realized. Sometimes leadership means: Wrong part delivered? Use what’s available on-site. Not 100% clean? Document, continue, fix later. Problem? Solve it. Don’t stop the work. 𝗧𝗵𝗲 𝘄𝗼𝗿𝘀𝘁 𝘁𝗵𝗶𝗻𝗴 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗱𝗼: 𝗦𝘁𝗼𝗽 𝘁𝗵𝗲 𝘀𝘁𝗲𝗮𝗺 𝘁𝗿𝗮𝗶𝗻. I’m not looking for textbook answers! I want real moments. When did you say: “Enough planning! We’re building now.” Let’s hear your stories. No glossy answers, just real-world lessons. #EPC #Leadership #ProjectExecution #Overengineering #SiloThinking #Construction #Solar #Pragmatism #RealTalk

Everyone's building AI agents, but few understand the Agentic frameworks that power them. These two distinct frameworks are the most used frameworks in 2025, and they aren't competitors but complementary approaches to agent development: 𝗻𝟴𝗻 (𝗩𝗶𝘀𝘂𝗮𝗹 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻) - Creates visual connections between AI agents and business tools - Flow: Trigger → AI Agent → Tools/APIs → Action - Solves integration complexity and enables rapid deployment - Think of it as the visual orchestrator connecting AI to your entire tech stack 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 (𝗚𝗿𝗮𝗽𝗵-𝗯𝗮𝘀𝗲𝗱 𝗔𝗴𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻) by LangChain - Enables stateful, cyclical agent workflows with precise control - Flow: State → Agents → Conditional Logic → State (cycles) - Solves complex reasoning and multi-step agent coordination - Think of it as the brain that manages sophisticated agent decision-making Beyond technicality, each framework has its core strengths. 𝗪𝗵𝗲𝗻 𝘁𝗼 𝘂𝘀𝗲 𝗻𝟴𝗻: - Integrating AI agents with existing business tools - Building customer support automation - Creating no-code AI workflows for teams - Needing quick deployment with 700+ integrations 𝗪𝗵𝗲𝗻 𝘁𝗼 𝘂𝘀𝗲 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵: - Building complex multi-agent reasoning systems - Creating enterprise-grade AI applications - Developing agents with cyclical workflows - Needing fine-grained state management Both frameworks are gaining significant traction: 𝗻𝟴𝗻 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺: - Visual workflow builder for non-developers - Self-hostable open-source option - Strong business automation community 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺: - Full LangChain ecosystem integration - LangSmith observability and debugging - Advanced state persistence capabilities Top AI solutions integrate both n8n and LangGraph to maximize their potential. - Use n8n for visual orchestration and business tool integration - Use LangGraph for complex agent logic and state management - Think in layers: business automation AND sophisticated reasoning Over to you: What AI agent use case would you build - one that needs visual simplicity (n8n) or complex orchestration (LangGraph)?

At Amazon, we would often spend months working on a single paragraph of the PR/FAQ for a new product idea. This was the "problem paragraph". Done well, it could lead to a successful product. Done wrong, it will lead to failure. Here is how to write a successful problem paragraph: The “problem paragraph” defines the customer problem you’re solving. Without this, you will build a product that doesn’t address a customer pain point. It shows whether you truly understand your customer's needs, not just your company’s capabilities. To write this paragraph, start by precisely identifying the customer segment that will be served by your product. Great products are built for specific people with specific needs. For instance, designing a car for single urban professionals under 35 differs significantly from designing for suburban families with three kids and a dog. If you think your product is for everyone, you’re mistaken. A strong way to begin your paragraph is: “Today, [customer segment] has [problem], which they currently solve using [methods A, B, and C]…” Next, quantify the problem: → How large is the segment? (e.g., 17 million households) → What methods do they use? (e.g., 45% use A, 25% use B, 30% use C) → What are the tradeoffs? (e.g., speed, cost, quality) Here’s an example for a hypothetical robot vacuum product: “Today, 15 million busy urban and suburban professionals earning between $100,000 and $200,000 struggle to find the time and energy to keep their homes clean. Approximately 30% of these households use traditional vacuuming, which requires up to 2 hours per week. 55% hire a cleaner at a minimum of $50/week, and 15% use robot vacuums that cost $600 plus $100/year in maintenance, while leaving behind up to 30% of dust and dirt.” This problem paragraph quantifies the customer problem in terms of money, time, and other metrics where possible (in this case, the dust and dirt left behind). The problem should always be quantified; otherwise, how can you assess the potential value of a product that solves it? Well-defined customer problems are built on data-based insights. Insights are gleaned from swimming in data and metrics. This includes customer usage metrics, process or operations metrics, user interviews, demographic data, customer feedback, customer support data and anecdotes. The more data-based and specific your insight, the more accurate and helpful your problem paragraph will be. This is why the process can take months. However, distilling these quantified insights into a single paragraph gives you the best chance at building a truly useful product. At Amazon, this paragraph was always the most debated section in a PR/FAQ. This is because getting the problem wrong is the worst mistake you can make in building a product. Everywhere else, you can pivot. But if the problem is incorrectly diagnosed, nothing else matters. (cont. in comments)

Your Head of Product will tell you this: The best PMs aren’t product people. The best PMs are business people. Early in my career, I thought being a strong PM meant: ✅ Clean roadmaps ✅ On-time releases ✅ Backlog grooming like a pro I checked every box—and still missed the mark. Because none of that matters if the product doesn’t drive the business. Old way: PMs manage features, coordinate teams, and keep the engine running. New way: PMs challenge assumptions, prioritize by impact, and own outcomes—not just outputs. Before anything goes on the roadmap, ask: - What business metric does this move? - What customer problem does it solve? - Why now? When you start thinking like a business owner—not just a product owner—everything changes. Here are 3 ways to make that shift: ✅ Take the initiative to drive action. Don’t wait for direction—own the next move. -> Frame problems, not just solutions. -> Bring data and customer insights to support your case. -> Proactively align with cross-functional partners. 💡 Actionable step: Use a BLUF (Bottom Line Up Front) to pitch new ideas: - What we’re proposing - Why it matters to the business - What we need to move forward ✅ Ensure the team knows the vision you’re pursuing. People don’t rally behind features—they rally behind purpose. -> Set clear outcomes, not just outputs. -> Anchor sprints to customer impact. -> Tell the story behind the roadmap. 💡 Actionable step: Start each sprint with a one-liner: "This week, we’re solving this problem for this customer because it supports this business goal." ✅ Prioritize by business impact. Great PMs don’t chase effort—they chase outcomes. -> Tie every feature to a metric that matters. -> Cut what doesn’t move the needle. -> Make tradeoffs visible and deliberate. 💡 Actionable step: Make sure every feature on the roadmap is linked to a prioritized strategic initiative. If it doesn’t ladder up, it doesn’t ship. Final thought: You don’t need an MBA. But you do need to think like a GM. -- 👋 I’m Ron Yang, a product leader and advisor. Follow me for insights on product leadership & strategy.