Fireside Product Management

TLDR: It was Claude :-)When I set out to compare ChatGPT, Claude, Gemini, Grok, and ChatPRD for writing Product Requirement Documents, I figured they’d all be roughly equivalent. Maybe some subtle variations in tone or structure, but nothing earth-shattering. They’re all built on similar transformer architectures, trained on massive datasets, and marketed as capable of handling complex business writing.What I discovered over 45 minutes of hands-on testing revealed not just which tools are better for PRD creation, but why they’re better, and more importantly, how you should actually be using AI to accelerate your product work without sacrificing quality or strategic thinking.If you’re an early or mid-career PM in Silicon Valley, this matters to you. Because here’s the uncomfortable truth: your peers are already using AI to write PRDs, analyze features, and generate documentation. The question isn’t whether to use these tools. The question is whether you’re using the right ones most effectively.So let me walk you through exactly what I did, what I learned, and what you should do differently.The Setup: A Real-World Test CaseHere’s how I structured the experiment. As I said at the beginning of my recording, “We are back in the Fireside PM podcast and I did that review of the ChatGPT browser and people seemed to like it and then I asked, uh, in a poll, I think it was a LinkedIn poll maybe, what should my next PM product review be? And, people asked for ChatPRD.”So I had my marching orders from the audience. But I wanted to make this more comprehensive than just testing ChatPRD in isolation. I opened up five tabs: ChatGPT, Claude, Gemini, Grok, and ChatPRD.For the test case, I chose something realistic and relevant: an AI-powered tutor for high school students. Think KhanAmigo or similar edtech platforms. This gave me a concrete product scenario that’s complex enough to stress-test these tools but straightforward enough that I could iterate quickly.But here’s the critical part that too many PMs get wrong when they start using AI for product work: I didn’t just throw a single sentence at these tools and expect magic.The “Back of the Napkin” Approach: Why You Still Need to Think“I presume everybody agrees that you should have some formulated thinking before you dump it into the chatbot for your PRD,” I noted early in my experiment. “I suppose in the future maybe you could just do, like, a one-sentence prompt and come out with the perfect PRD because it would just know everything about you and your company in the context, but for now we’re gonna do this more, a little old-school AI approach where we’re gonna do some original human thinking.”This is crucial. I see so many PMs, especially those newer to the field, treat AI like a magic oracle. They type in “Write me a PRD for a social feature” and then wonder why the output is generic, unfocused, and useless.Your job as a PM isn’t to become obsolete. It’s to become more effective. And that means doing the strategic thinking work that AI cannot do for you.So I started in Google Docs with what I call a “back of the napkin” PRD structure. Here’s what I included:Why: The strategic rationale. In this case: “Want to complement our existing edtech business with a personalized AI tutor, uh, want to maintain position industry, and grow through innovation. on mission for learners.”Target User: Who are we building for? “High school students interested in improving their grades and fundamentals. Fundamental knowledge topics. Specifically science and math. Students who are not in the top ten percent, nor in the bottom ten percent.”This is key—I got specific. Not just “students,” but students in the middle 80%. Not just “any subject,” but science and math. This specificity is what separates useful AI output from garbage.Problem to Solve: What’s broken? “Students want better grades. Students are impatient. Students currently use AI just for finding the answers and less to, uh, understand concepts and practice using them.”Key Elements: The feature set and approach.Success Metrics: How we’d measure success.Now, was this a perfectly polished PRD outline? Hell no. As you can see from my transcript, I was literally thinking out loud, making typos, restructuring on the fly. But that’s exactly the point. I put in maybe 10-15 minutes of human strategic thinking. That’s all it took to create a foundation that would dramatically improve what came out of the AI tools.Round One: Generating the Full PRDWith my back-of-the-napkin outline ready, I copied it into each tool with a simple prompt asking them to expand it into a more complete PRD.ChatGPT: The Reliable GeneralistChatGPT gave me something that was... fine. Competent. Professional. But also deeply uninspiring.The document it produced checked all the boxes. It had the sections you’d expect. The writing was clear. But when I read it, I couldn’t shake the feeling that I was reading something that could have been written for literally any product in any company. It felt like “an average of everything out there,” as I noted in my evaluation.Here’s what ChatGPT did well: It understood the basic structure of a PRD. It generated appropriate sections. The grammar and formatting were clean. If you needed to hand something in by EOD and had literally no time for refinement, ChatGPT would save you from complete embarrassment.But here’s what it lacked: Depth. Nuance. Strategic thinking that felt connected to real product decisions. When it described the target user, it used phrases that could apply to any edtech product. When it outlined success metrics, they were the obvious ones (engagement, retention, test scores) without any interesting thinking about leading indicators or proxy metrics.The problem with generic output isn’t that it’s wrong, it’s that it’s invisible. When you’re trying to get buy-in from leadership or alignment from engineering, you need your PRD to feel specific, considered, and connected to your company’s actual strategy. ChatGPT’s output felt like it was written by someone who’d read a lot of PRDs but never actually shipped a product.One specific example: When I asked for success metrics, ChatGPT gave me “Student engagement rate, Time spent on platform, Test score improvement.” These aren’t wrong, but they’re lazy. They don’t show any thinking about what specifically matters for an AI tutor versus any other educational product. Compare that to Claude’s output, which got more specific about things like “concept mastery rate” and “question-to-understanding ratio.”Actionable Insight: Use ChatGPT when you need fast, serviceable documentation that doesn’t need to be exceptional. Think: internal updates, status reports, routine communications. Don’t rely on it for strategic documents where differentiation matters. If you do use ChatGPT for important documents, treat its output as a starting point that needs significant human refinement to add strategic depth and company-specific context.Gemini: Better Than ExpectedGoogle’s Gemini actually impressed me more than I anticipated. The structure was solid, and it had a nice balance of detail without being overwhelming.What Gemini got right: The writing had a nice flow to it. The document felt organized and logical. It did a better job than ChatGPT at providing specific examples and thinking through edge cases. For instance, when describing the target user, it went beyond demographics to consider behavioral characteristics and motivations.Gemini also showed some interesting strategic thinking. It considered competitive positioning more thoughtfully than ChatGPT and proposed some differentiation angles that weren’t in my original outline. Good AI tools should add insight, not just regurgitate your input with better formatting.But here’s where it fell short: the visual elements. When I asked for mockups, Gemini produced images that looked more like stock photos than actual product designs. They weren’t terrible, but they weren’t compelling either. They had that AI-generated sheen that makes it obvious they came from an image model rather than a designer’s brain.For a PRD that you’re going to use internally with a team that already understands the context, Gemini’s output would work well. The text quality is strong enough, and if you’re in the Google ecosystem (Docs, Sheets, Meet, etc.), the integration is seamless. You can paste Gemini’s output directly into Google Docs and continue iterating there.But if you need to create something compelling enough to win over skeptics or secure budget, Gemini falls just short. It’s good, but not great. It’s the solid B+ student: reliably competent but rarely exceptional.Actionable Insight: Gemini is a strong choice if you’re working in the Google ecosystem and need good integration with Docs, Sheets, and other Google Workspace tools. The quality is sufficient for most internal documentation needs. It’s particularly good if you’re working with cross-functional partners who are already in Google Workspace. You can share and collaborate on AI-generated drafts without friction. But don’t expect visual mockups that will wow anyone, and plan to add your own strategic polish for high-stakes documents.Grok: Not Ready for Prime TimeLet’s just say my expectations were low, and Grok still managed to underdeliver. The PRD felt thin, generic, and lacked the depth you need for real product work.“I don’t have high expectations for grok, unfortunately,” I said before testing it. Spoiler alert: my low expectations were validated.Actionable Insight: Skip Grok for product documentation work right now. Maybe it’ll improve, but as of my testing, it’s simply not competitive with the other options. It felt like 1-2 years behind the others.ChatPRD: The Specialized ToolNow this was interesting. ChatPRD is purpose-built for PRDs, using foundational models underneath but with specific tuning and structure for product documentation.The result? The structure was logical, the depth was appropriate, and it included elements that showed understanding of what actually matters in a PRD. As I reflected: “Cause this one feels like, A human wrote this PRD.”The interface guides you through the process more deliberately than just dumping text into a general chat interface. It asks clarifying questions. It structures the output more thoughtfully.Actionable Insight: If you’re a technical lead without a dedicated PM, or you’re a PM who wants a more structured approach to using AI for PRDs, ChatPRD is worth the specialized focus. It’s particularly good when you need something that feels authentic enough to share with stakeholders without heavy editing.Claude: The Clear WinnerBut the standout performer, and I’m ranking these, was Claude.“I think we know that for now, I’m gonna say Claude did the best job,” I concluded after all the testing. Claude produced the most comprehensive, thoughtful, and strategically sound PRD. But what really set it apart were the concept mocks.When I asked each tool to generate visual mockups of the product, Claude produced HTML prototypes that, while not fully functional, looked genuinely compelling. They had thoughtful UI design, clear information architecture, and felt like something that could actually guide development.“They were, like, closer to, like, what a Lovable would produce or something like that,” I noted, referring to the quality of low-fidelity prototypes that good designers create.The text quality was also superior: more nuanced, better structured, and with more strategic depth. It felt like Claude understood not just what a PRD should contain, but why it should contain those elements.Actionable Insight: For any PRD that matters, meaning anything you’ll share with leadership, use to get buy-in, or guide actual product development, you might as well start with Claude. The quality difference is significant enough that it’s worth using Claude even if you primarily use another tool for other tasks.Final Rankings: The Definitive HierarchyAfter testing all five tools on multiple dimensions: initial PRD generation, visual mockups, and even crafting a pitch paragraph for a skeptical VP of Engineering, here’s my final ranking:* Claude - Best overall quality, most compelling mockups, strongest strategic thinking* ChatPRD - Best for structured PRD creation, feels most “human”* Gemini - Solid all-around performance, good Google integration* ChatGPT - Reliable but generic, lacks differentiation* Grok - Not competitive for this use case“I’d probably say Claude, then chat PRD, then Gemini, then chat GPT, and then Grock,” I concluded.The Deeper Lesson: Garbage In, Garbage Out (Still Applies)But here’s what matters more than which tool wins: the realization that hit me partway through this experiment.“I think it really does come down to, like, you know, the quality of the prompt,” I observed. “So if our prompt were a little more detailed, all that were more thought-through, then I’m sure the output would have been better. But as you can see we didn’t really put in brain trust prompting here. Just a little bit of, kind of hand-wavy prompting, but a little better than just one or two sentences.”And we still got pretty good results.This is the meta-insight that should change how you approach AI tools in your product work: The quality of your input determines the quality of your output, but the baseline quality of the tool determines the ceiling of what’s possible.No amount of great prompting will make Grok produce Claude-level output. But even mediocre prompting with Claude will beat great prompting with lesser tools.So the dual strategy is:* Use the best tool available (currently Claude for PRDs)* Invest in improving your prompting skills ideally with as much original and insightful human, company aware, and context aware thinking as possible.Real-World Workflows: How to Actually Use This in Your Day-to-Day PM WorkTheory is great. Here’s how to incorporate these insights into your actual product management workflows.The Weekly Sprint Planning WorkflowEvery PM I know spends hours each week preparing for sprint planning. You need to refine user stories, clarify acceptance criteria, anticipate engineering questions, and align with design and data science. AI can compress this work significantly.Here’s an example workflow:Monday morning (30 minutes):* Review upcoming priorities and open your rough notes/outline in Google Docs* Open Claude and paste your outline with this prompt:“I’m preparing for sprint planning. Based on these priorities [paste notes], generate detailed user stories with acceptance criteria. Format each as: User story, Business context, Technical considerations, Acceptance criteria, Dependencies, Open questions.”Monday afternoon (20 minutes):* Review Claude’s output critically* Identify gaps, unclear requirements, or missing context* Follow up with targeted prompts:“The user story about authentication is too vague. Break it down into separate stories for: social login, email/password, session management, and password reset. For each, specify security requirements and edge cases.”Tuesday morning (15 minutes):* Generate mockups for any UI-heavy stories:“Create an HTML mockup for the login flow showing: landing page, social login options, email/password form, error states, and success redirect.”* Even if the HTML doesn’t work perfectly, it gives your designers a starting pointBefore sprint planning (10 minutes):* Ask Claude to anticipate engineering questions:“Review these user stories as if you’re a senior engineer. What questions would you ask? What concerns would you raise about technical feasibility, dependencies, or edge cases?”* This preparation makes you look thoughtful and helps the meeting run smoothlyTotal time investment: ~75 minutes. Typical time saved: 3-4 hours compared to doing this manually.The Stakeholder Alignment WorkflowGetting alignment from multiple stakeholders (product leadership, engineering, design, data science, legal, marketing) is one of the hardest parts of PM work. AI can help you think through different stakeholder perspectives and craft compelling communications for each.Here’s how:Step 1: Map your stakeholders (10 minutes)Create a quick table in a doc:Stakeholder | Primary Concern | Decision Criteria | Likely Objections VP Product | Strategic fit, ROI | Company OKRs, market opportunity | Resource allocation vs other priorities VP Eng | Technical risk, capacity | Engineering capacity, tech debt | Complexity, unclear requirements Design Lead | User experience | User research, design principles | Timeline doesn’t allow proper design process Legal | Compliance, risk | Regulatory requirements | Data privacy, user consent flowsStep 2: Generate stakeholder-specific communications (20 minutes)For each key stakeholder, ask Claude:“I need to pitch this product idea to [Stakeholder]. Based on this PRD, create a 1-page brief addressing their primary concern of [concern from your table]. Open with the specific value for them, address their likely objection of [objection], and close with a clear ask. Tone should be [professional/technical/strategic] based on their role.”Then you’ll have customized one-pagers for your pre-meetings with each stakeholder, dramatically increasing your alignment rate.Step 3: Synthesize feedback (15 minutes)After gathering stakeholder input, ask Claude to help you synthesize:“I got the following feedback from stakeholders: [paste feedback]. Identify: (1) Common themes, (2) Conflicting requirements, (3) Legitimate concerns vs organizational politics, (4) Recommended compromises that might satisfy multiple parties.”This pattern-matching across stakeholder feedback is something AI does really well and saves you hours of mental processing.The Quarterly Planning WorkflowQuarterly or annual planning is where product strategy gets real. You need to synthesize market trends, customer feedback, technical capabilities, and business objectives into a coherent roadmap. AI can accelerate this dramatically.Six weeks before planning:* Start collecting input (customer interviews, market research, competitive analysis, engineering feedback)* Don’t wait until the last minuteFour weeks before planning:Dump everything into Claude with this structure:“I’m creating our Q2 roadmap. Context:* Business objectives: [paste from leadership]* Customer feedback themes: [paste synthesis]* Technical capabilities/constraints: [paste from engineering]* Competitive landscape: [paste analysis]* Current product gaps: [paste from your analysis]Generate 5 strategic themes that could anchor our Q2 roadmap. For each theme:* Strategic rationale (how it connects to business objectives)* Key initiatives (2-3 major features/projects)* Success metrics* Resource requirements (rough estimate)* Risks and mitigations* Customer segments addressed”This gives you a strategic framework to react to rather than starting from a blank page.Three weeks before planning:Iterate on the most promising themes:“Deep dive on Theme 3. Generate:* Detailed initiative breakdown* Dependencies on platform/infrastructure* Phasing options (MVP vs full build)* Go-to-market considerations* Data requirements* Open questions requiring research”Two weeks before planning:Pressure-test your thinking:“Play devil’s advocate on this roadmap. What are the strongest arguments against each initiative? What am I likely missing? What failure modes should I plan for?”This adversarial prompting forces you to strengthen weak points before your leadership reviews it.One week before planning:Generate your presentation:“Create an executive presentation for this roadmap. Structure: (1) Market context and strategic imperative, (2) Q2 themes and initiatives, (3) Expected outcomes and metrics, (4) Resource requirements, (5) Key risks and mitigations, (6) Success criteria for decision. Make it compelling but data-driven. Tone: confident but not overselling.”Then add your company-specific context, visual brand, and personal voice.The Customer Research WorkflowAI can’t replace talking to customers, but it can help you prepare better questions, analyze feedback more systematically, and identify patterns faster.Before customer interviews:“I’m interviewing customers about [topic]. Generate:* 10 open-ended questions that avoid leading the witness* 5 follow-up questions for each main question* Common cognitive biases I should watch for* A framework for categorizing responses”This prep work helps you conduct better interviews.After interviews:“I conducted 15 customer interviews. Here are the key quotes: [paste anonymized quotes]. Identify:* Recurring themes and patterns* Surprising insights that contradict our assumptions* Segments with different needs* Implied needs customers didn’t articulate directly* Recommended next steps for validation”AI is excellent at pattern-matching across qualitative data at scale.The Crisis Management WorkflowSomething broke. The site is down. Data was lost. A feature shipped with a critical bug. You need to move fast.Immediate response (5 minutes):“Critical incident. Details: [brief description]. Generate:* Incident classification (Sev 1-4)* Immediate stakeholders to notify* Draft customer communication (honest, apologetic, specific about what happened and what we’re doing)* Draft internal communication for leadership* Key questions to ask engineering during investigation”Having these drafted in 5 minutes lets you focus on coordination and decision-making rather than wordsmithing.Post-incident (30 minutes):“Write a post-mortem based on this incident timeline: [paste timeline]. Include:* What happened (technical details)* Root cause analysis* Impact quantification (users affected, revenue impact, time to resolution)* What went well in our response* What could have been better* Specific action items with owners and deadlines* Process changes to prevent recurrence Tone: Blameless, focused on learning and improvement.”This gives you a strong first draft to refine with your team.Common Pitfalls: What Not to Do with AI in Product ManagementNow let’s talk about the mistakes I see PMs making with AI tools. Pitfall #1: Treating AI Output as FinalThe biggest mistake is copy-pasting AI output directly into your PRD, roadmap presentation, or stakeholder email without critical review.The result? Documents that are grammatically perfect but strategically shallow. Presentations that sound impressive but don’t hold up under questioning. Emails that are professionally worded but miss the subtext of organizational politics.The fix: Always ask yourself:* Does this reflect my actual strategic thinking, or generic best practices?* Would my CEO/engineering lead/biggest customer find this compelling and specific?* Are there company-specific details, customer insights, or technical constraints that only I know?* Does this sound like me, or like a robot?Add those elements. That’s where your value as a PM comes through.Pitfall #2: Using AI as a Crutch Instead of a ToolSome PMs use AI because they don’t want to think deeply about the product. They’re looking for AI to do the hard work of strategy, prioritization, and trade-off analysis.This never works. AI can help you think more systematically, but it can’t replace thinking.If you find yourself using AI to avoid wrestling with hard questions (”Should we build X or Y?” “What’s our actual competitive advantage?” “Why would customers switch from the incumbent?”), you’re using it wrong.The fix: Use AI to explore options, not to make decisions. Generate three alternatives, pressure-test each one, then use your judgment to decide. The AI can help you think through implications, but you’re still the one choosing.Pitfall #3: Not IteratingGetting mediocre AI output and just accepting it is a waste of the technology’s potential.The PMs who get exceptional results from AI are the ones who iterate. They generate an initial response, identify what’s weak or missing, and ask follow-up questions. They might go through 5-10 iterations on a key section of a PRD.Each iteration is quick (30 seconds to type a follow-up prompt, 30 seconds to read the response), but the cumulative effect is dramatically better output.The fix: Budget time for iteration. Don’t try to generate a complete, polished PRD in one prompt. Instead, generate a rough draft, then spend 30 minutes iterating on specific sections that matter most.Pitfall #4: Ignoring the Political and Human ContextAI tools have no understanding of organizational politics, interpersonal relationships, or the specific humans you’re working with.They don’t know that your VP of Engineering is burned out and skeptical of any new initiatives. They don’t know that your CEO has a personal obsession with a specific competitor. They don’t know that your lead designer is sensitive about not being included early enough in the process.If you use AI-generated communications without layering in this human context, you’ll create perfectly worded documents that land badly because they miss the subtext.The fix: After generating AI content, explicitly ask yourself: “What human context am I missing? What relationships do I need to consider? What political dynamics are in play?” Then modify the AI output accordingly.Pitfall #5: Over-Relying on a Single ToolDifferent AI tools have different strengths. Claude is great for strategic depth, ChatPRD is great for structure, Gemini integrates well with Google Workspace.If you only ever use one tool, you’re missing opportunities to leverage different strengths for different tasks.The fix: Keep 2-3 tools in your toolkit. Use Claude for important PRDs and strategic documents. Use Gemini for quick internal documentation that needs to integrate with Google Docs. Use ChatPRD when you want more guided structure. Match the tool to the task.Pitfall #6: Not Fact-Checking AI OutputAI tools hallucinate. They make up statistics, misrepresent competitors, and confidently state things that aren’t true. If you include those hallucinations in a PRD that goes to leadership, you look incompetent.The fix: Fact-check everything, especially:* Statistics and market data* Competitive feature claims* Technical capabilities and limitations* Regulatory and compliance requirementsIf the AI cites a number or makes a factual claim, verify it independently before including it in your document.The Meta-Skill: Prompt Engineering for PMsLet’s zoom out and talk about the underlying skill that makes all of this work: prompt engineering.This is a real skill. The difference between a mediocre prompt and a great prompt can be 10x difference in output quality. And unlike coding or design, where there’s a steep learning curve, prompt engineering is something you can get good at quickly.Principle 1: Provide Context Before InstructionsBad prompt:“Write a PRD for an AI tutor”Good prompt:“I’m a PM at an edtech company with 2M users, primarily high school students. We’re exploring an AI tutor feature to complement our existing video content library and practice problems. Our main competitors are Khan Academy and Course Hero. Our differentiation is personalized learning paths based on student performance data.Write a PRD for an AI tutor feature targeting students in the middle 80% academically who struggle with science and math.”The second prompt gives Claude the context it needs to generate something specific and strategic rather than generic.Principle 2: Specify Format and ConstraintsBad prompt:“Generate success metrics”Good prompt:“Generate 5-7 success metrics for this feature. Include a mix of:* Leading indicators (early signals of success)* Lagging indicators (definitive success measures)* User behavior metrics* Business impact metricsFor each metric, specify: name, definition, target value, measurement method, and why it matters.”The structure you provide shapes the structure you get back.Principle 3: Ask for Multiple OptionsBad prompt:“What should our Q2 priorities be?”Good prompt:“Generate 3 different strategic approaches for Q2:* Option A: Focus on user acquisition* Option B: Focus on engagement and retention* Option C: Focus on monetizationFor each option, detail: key initiatives, expected outcomes, resource requirements, risks, and recommendation for or against.”Asking for multiple options forces the AI (and forces you) to think through trade-offs systematically.Principle 4: Specify Audience and ToneBad prompt:“Summarize this PRD”Good prompt:“Create a 1-paragraph summary of this PRD for our skeptical VP of Engineering. Tone: Technical, concise, addresses engineering concerns upfront. Focus on: technical architecture, resource requirements, risks, and expected engineering effort. Avoid marketing language.”The audience and tone specification ensures the output will actually work for your intended use.Principle 5: Use Iterative RefinementDon’t try to get perfect output in one prompt. Instead:First prompt: Generate rough draft Second prompt: “This is too generic. Add specific examples from [our company context].” Third prompt: “The technical section is weak. Expand with architecture details and dependencies.” Fourth prompt: “Good. Now make it 30% more concise while keeping the key details.”Each iteration improves the output incrementally.Let me break down the prompting approach that worked in this experiment, because this is immediately actionable for your work tomorrow.Strategy 1: The Structured Outline ApproachDon’t go from zero to full PRD in one prompt. Instead:* Start with strategic thinking - Spend 10-15 minutes outlining why you’re building this, who it’s for, and what problem it solves* Get specific - Don’t say “users,” say “high school students in the middle 80% of academic performance”* Include constraints - Budget, timeline, technical limitations, competitive landscape* Dump your outline into the AI - Now ask it to expand into a full PRD* Iterate section by section - Don’t try to perfect everything at onceThis is exactly what I did in my experiment, and even with my somewhat sloppy outline, the results were dramatically better than they would have been with a single-sentence prompt.Strategy 2: The Comparative Analysis PatternOne technique I used that worked particularly well: asking each tool to do the same specific task and comparing results.For example, I asked all five tools: “Please compose a one paragraph exact summary I can share over DM with a highly influential VP of engineering who is generally a skeptic but super smart.”This forced each tool to synthesize the entire PRD into a compelling pitch while accounting for a specific, challenging audience. The variation in quality was revealing—and it gave me multiple options to choose from or blend together.Actionable tip: When you need something critical (a pitch, an executive summary, a key decision framework), generate it with 2-3 different AI tools and take the best elements from each. This “ensemble approach” often produces better results than any single tool.Strategy 3: The Iterative Refinement LoopDon’t treat the AI output as final. Use it as a first draft that you then refine through conversation with the AI.After getting the initial PRD, I could have asked follow-up questions like:* “What’s missing from this PRD?”* “How would you strengthen the success metrics section?”* “Generate 3 alternative approaches to the core feature set”Each iteration improves the output and, more importantly, forces me to think more deeply about the product.What This Means for Your CareerIf you’re an early or mid-career PM reading this, you might be thinking: “Great, so AI can write PRDs now. Am I becoming obsolete?”Absolutely not. But your role is evolving, and understanding that evolution is critical.The PMs who will thrive in the AI era are those who:* Excel at strategic thinking - AI can generate options, but you need to know which options align with company strategy, customer needs, and technical feasibility* Master the art of prompting - This is a genuine skill that separates mediocre AI users from exceptional ones* Know when to use AI and when not to - Some aspects of product work benefit enormously from AI. Others (user interviews, stakeholder negotiation, cross-functional relationship building) require human judgment and empathy* Can evaluate AI output critically - You need to spot the hallucinations, the generic fluff, and the strategic misalignments that AI inevitably producesThink of AI tools as incredibly capable interns. They can produce impressive work quickly, but they need direction, oversight, and strategic guidance. Your job is to provide that guidance while leveraging their speed and breadth.The Real-World Application: What to Do Monday MorningLet’s get tactical. Here’s exactly how to apply these insights to your actual product work:For Your Next PRD:* Block 30 minutes for strategic thinking - Write your back-of-the-napkin outline in Google Docs or your tool of choice* Open Claude (or ChatPRD if you want more structure)* Copy your outline with this prompt:“I’m a product manager at [company] working on [product area]. I need to create a comprehensive PRD based on this outline. Please expand this into a complete PRD with the following sections: [list your preferred sections]. Make it detailed enough for engineering to start breaking down into user stories, but concise enough for leadership to read in 15 minutes. [Paste your outline]”* Review the output critically - Look for generic statements, missing details, or strategic misalignments* Iterate on specific sections:“The success metrics section is too vague. Please provide 3-5 specific, measurable KPIs with target values and explanation of why these metrics matter.”* Generate supporting materials:“Create a visual mockup of the core user flow showing the key interaction points.”* Synthesize the best elements - Don’t just copy-paste the AI output. Use it as raw material that you shape into your final documentFor Stakeholder Communication:When you need to pitch something to leadership or engineering:* Generate 3 versions of your pitch using different tools (Claude, ChatPRD, and one other)* Compare them for:* Clarity and conciseness* Strategic framing* Compelling value proposition* Addressing likely objections* Blend the best elements into your final version* Add your personal voice - This is crucial. AI output often lacks personality and specific company context. Add that yourself.For Feature Prioritization:AI tools can help you think through trade-offs more systematically:“I’m deciding between three features for our next release: [Feature A], [Feature B], and [Feature C]. For each feature, analyze: (1) Estimated engineering effort, (2) Expected user impact, (3) Strategic alignment with making our platform the go-to solution for [your market], (4) Risk factors. Then recommend a prioritization with rationale.”This doesn’t replace your judgment, but it forces you to think through each dimension systematically and often surfaces considerations you hadn’t thought of.The Uncomfortable Truth About AI and Product ManagementLet me be direct about something that makes many PMs uncomfortable: AI will make some PM skills less valuable while making others more valuable.Less valuable:* Writing boilerplate documentation* Creating standard frameworks and templates* Generating routine status updates* Synthesizing information from existing sourcesMore valuable:* Strategic product vision and roadmapping* Deep customer empathy and insight generation* Cross-functional leadership and influence* Critical evaluation of options and trade-offs* Creative problem-solving for novel situationsIf your PM role primarily involves the first category of tasks, you should be concerned. But if you’re focused on the second category while leveraging AI for the first, you’re going to be exponentially more effective than your peers who resist these tools.The PMs I see succeeding aren’t those who can write the best PRD manually. They’re those who can write the best PRD with AI assistance in one-tenth the time, then use the saved time to talk to more customers, think more deeply about strategy, and build stronger cross-functional relationships.Advanced Techniques: Beyond Basic PRD GenerationOnce you’ve mastered the basics, here are some advanced applications I’ve found valuable:Competitive Analysis at Scale“Research our top 5 competitors in [market]. For each one, analyze: their core value proposition, key features, pricing strategy, target customer, and likely product roadmap based on recent releases and job postings. Create a comparison matrix showing where we have advantages and gaps.”Then use web search tools in Claude or Perplexity to fact-check and expand the analysis.Scenario Planning“We’re considering three strategic directions for our product: [Direction A], [Direction B], [Direction C]. For each direction, map out: likely customer adoption curve, required technical investments, competitive positioning in 12 months, and potential pivots if the hypothesis proves wrong. Then identify the highest-risk assumptions we should test first for each direction.”This kind of structured scenario thinking is exactly what AI excels at—generating multiple well-reasoned perspectives quickly.User Story GenerationAfter your PRD is solid:“Based on this PRD, generate a complete set of user stories following the format ‘As a [user type], I want to [action] so that [benefit].’ Include acceptance criteria for each story. Organize them into epics by functional area.”This can save your engineering team hours of grooming meetings.The Tools Will Keep Evolving. Your Process Shouldn’tHere’s something important to remember: by the time you read this, the specific rankings might have shifted. Maybe ChatGPT-5 has leapfrogged Claude. Maybe a new specialized tool has emerged.But the core principles won’t change:* Do strategic thinking before touching AI* Use the best tool available for your specific task* Iterate and refine rather than accepting first outputs* Blend AI capabilities with human judgment* Focus your time on the uniquely human aspects of product managementThe specific tools matter less than your process for using them effectively.A Final Experiment: The Skeptical VP TestI want to share one more insight from my testing that I think is particularly relevant for early and mid-career PMs.Toward the end of my experiment, I gave each tool this prompt: “Please compose a one paragraph exact summary I can share over DM with a highly influential VP of engineering who is generally a skeptic but super smart.”This is such a realistic scenario. How many times have you needed to pitch an idea to a skeptical technical leader via Slack or email? Someone who’s brilliant, who’s seen a thousand product ideas fail, and who can spot b******t from a mile away?The quality variation in the responses was fascinating. ChatGPT gave me something that felt generic and safe. Gemini was better but still a bit too enthusiastic. Grok was... well, Grok.But Claude and ChatPRD both produced messages that felt authentic, technically credible, and appropriately confident without being overselling. They acknowledged the engineering challenges while framing the opportunity compellingly.The lesson: When the stakes are high and the audience is sophisticated, the quality of your AI tool matters even more. That skeptical VP can tell the difference between a carefully crafted message and AI-generated fluff. So can your CEO. So can your biggest customers.Use the best tools available, but more importantly, always add your own strategic thinking and authentic voice on top.Questions to Consider: A Framework for Your Own ExperimentsAs I wrapped up my Loom, I posed some questions to the audience that I’ll pose to you:“Let me know in the comments, if you do your PRDs using AI differently, do you start with back of the envelope? Do you say, oh no, I just start with one sentence, and then I let the chatbot refine it with me? Or do you go way more detailed and then use the chatbot to kind of pressure test it?”These aren’t rhetorical questions. Your answer reveals your approach to AI-augmented product work, and different approaches work for different people and contexts.For early-career PMs: I’d recommend starting with more detailed outlines. The discipline of thinking through your product strategy before touching AI will make you a stronger PM. You can always compress that process later as you get more experienced.For mid-career PMs: Experiment with different approaches for different types of documents. Maybe you do detailed outlines for major feature PRDs but use more iterative AI-assisted refinement for smaller features or updates. Find what optimizes your personal productivity while maintaining quality.For senior PMs and product leaders: Consider how AI changes what you should expect from your PM team. Should you be reviewing more AI-generated first drafts and spending more time on strategic guidance? Should you be training your team on effective AI usage? These are leadership questions worth grappling with.The Path Forward: Continuous ExperimentationMy experiment with these five AI tools took 45 minutes. But I’m not done experimenting.The field of AI-assisted product management is evolving rapidly. New tools launch monthly. Existing tools get smarter weekly. Prompting techniques that work today might be obsolete in three months.Your job, if you want to stay at the forefront of product management, is to continuously experiment. Try new tools. Share what works with your peers. Build a personal knowledge base of effective prompts and workflows. And be generous with what you learn. The PM community gets stronger when we share insights rather than hoarding them.That’s why I created this Loom and why I’m writing this post. Not because I have all the answers, but because I’m figuring it out in real-time and want to share the journey.A Personal Note on Coaching and ConsultingIf this kind of practical advice resonates with you, I’m happy to work with you directly.Through my pm coaching practice, I offer 1:1 executive, career, and product coaching for PMs and product leaders. We can dig into your specific challenges: whether that’s leveling up your AI workflows, navigating a career transition, or developing your strategic product thinking.I also work with companies (usually startups or incubation teams) on product strategy, helping teams figure out PMF for new explorations and improving their product management function.The format is flexible. Some clients want ongoing coaching, others prefer project-based consulting, and some just want a strategic sounding board for a specific decision. Whatever works for you.Reach out through tomleungcoaching.com if you’re interested in working together.OK. Enough pontificating. Let’s ship greatness. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit firesidepm.substack.com

The Interview That Sparked This EssayJoe Corkery and I worked together at Google years ago, and he has since gone on to build a venture-backed company tackling a real and systemic problem in healthcare communication. This essay is my attempt to synthesize that conversation. It is written for early and mid career PMs in Silicon Valley who want to get sharper at product judgment, market discovery, customer validation, and knowing the difference between encouragement and signal. If you feel like you have ever shipped something, presented it to customers, and then heard polite nodding instead of movement and urgency, this is for you.Joe’s Unusual Career ArcJoe’s background is not typical for a founder. He is a software engineer. And a physician. And someone who has led business development in the pharmaceutical industry. That multidisciplinary profile allowed him to see something that many insiders miss: healthcare is full of problems that everyone acknowledges, yet very few organizations are structurally capable of solving.When Joe joined Google Cloud in 2014, he helped start the healthcare and life sciences product org. Yet the timing was difficult. As he put it:“The world wasn’t ready or Google wasn’t ready to do healthcare.” So instead of building healthcare products right away, he spent two years working on security, compliance, and privacy. That detour will matter later, because it set the foundation for everything he is now doing at Jaide.Years later, he left Google to build a healthcare company focused initially on guided healthcare search, particularly for women’s health. The idea resonated emotionally. Every customer interview validated the need. Investors said it was important. Healthcare organizations nodded enthusiastically.And yet, there was no traction.This created a familiar and emotionally challenging founder dilemma:* When everyone is encouraging you* But no one will pay you or adopt early* How do you know if you are early, unlucky, or wrong?This is the question at the heart of product strategy.False Positives: Why Encouragement Is Not FeedbackIf you have worked as a PM or founder for more than a few weeks, you have encountered positive feedback that turned out to be meaningless. People love your idea. Executives praise your clarity. Customers tell you they would definitely use it. Friends offer supportive high-fives.But then nothing moves.As Joe put it:“Everyone wanted to be supportive. But that makes it hard to know whether you’re actually on the right path.” This is not because people are dishonest. It is because people are kind, polite, and socially conditioned to encourage enthusiasm. In Silicon Valley especially, we celebrate ambition. We praise risk-taking. We cheer for the founder-in-the-garage mythology. If someone tells you that your idea is flawed, they fear they are crushing your passion.So even when we explicitly ask for brutal honesty, people soften their answers.This is the false positive trap.And if you misread encouragement as traction, you can waste months or even years.The Small Framing Change That Changes EverythingJoe eventually realized that the problem was not the idea itself. The problem was how he was asking for feedback.When you present your idea as the idea, people naturally react supportively:* “That’s really interesting.”* “I could see that being useful.”* “This is definitely needed.”But when you instead present two competing ideas and ask someone to help you choose, you change the psychology of the conversation entirely.Joe explained it this way:“When we said, ‘We are building this. What do you think?’ people wanted to be encouraging. But when we asked, ‘We are choosing between these two products. Which one should we build?’ it gave them permission to actually critique.” This shift is subtle, but powerful. Suddenly:* People contrast.* Their reasoning surfaces.* Their hesitation becomes visible.* Their priorities emerge with clarity.By asking someone to choose between two ideas, you activate their decision-making brain instead of their supportive brain.It is no different from usability testing. If you show someone a screen and ask what they think, they are polite. If you give them a task and ask them to complete it, their actual friction appears immediately.In product discovery, friction is truth.How This Applies to PMs, Not Just FoundersYou may be thinking: this is interesting for entrepreneurs, but I work inside a company. I have stakeholders, OKRs, a roadmap, and a backlog that already feels too full.This technique is actually more relevant for PMs inside companies than for founders.Inside organizations, political encouragement is even more pervasive:* Leaders say they want innovation, but are risk averse.* Cross-functional partners smile in meetings, but quietly maintain objections.* Engineers nod when you present the roadmap, but may not believe in it.* Customers say they like your idea, but do not prioritize adoption.One of the most powerful tools you can use as a PM is explicitly framing your product decisions as explicit choices, rather than proposals seeking validation. For example:Instead of saying:“We are planning to build a new onboarding flow. Here is the design. Thoughts?”Say:“We are deciding between optimizing retention or acquisition next quarter. If we choose retention, the main lever is onboarding friction. Here are two possible approaches. Which outcome matters more to the business right now?”In the second framing:* The business goal is visible.* The tradeoff is unavoidable.* The decision owner is clear.* The conversation becomes real.This is how PMs build credibility and influence: not through slides or persuasion, but through framing decisions clearly.Jaide’s Pivot: From Health Search to AI TranslationThe result of Joe’s reframed feedback approach was unambiguous.Across dozens of conversations with healthcare executives and hospital leaders, one pattern emerged consistently:Translation was the urgent, budget-backed, economically meaningful problem.As Joe put it, after talking to more than 40 healthcare decision-makers:“Every single person told us to build the translation product. Not mostly. Not many. Every single one.” This kind of clarity is rare in product strategy. When you get it, you do not ignore it. You move.Jaide Health shifted its core focus to solving a very real, very measurable, and very painful problem in healthcare: the language gap affecting millions of patients.More than 25 million patients in the United States do not speak English well enough to communicate with clinicians. This leads to measurable harm:* Longer hospital stays* Increased readmission rates* Higher medical error rates* Lower comprehension of discharge instructionsThe status quo for translation relies on human interpreters who are expensive, limited, slow to schedule, and often unavailable after hours or in rare languages. Many clinicians, due to lack of resources, simply use Google Translate privately on their phones. They know this is not secure or compliant, but they feel like they have no better option.So Jaide built a platform that integrates compliance, healthcare-specific terminology, workflow embedding, custom glossaries, discharge summaries, and real-time accessibility.This is not simply “healthcare plus GPT”. It is targeted, workflow-integrated, risk-aware operational excellence.Product managers should study this pattern closely.The winning strategy was not inventing a new problem. It was solving a painful problem that everyone already agreed mattered.The Core PM Lesson: Focus on Problems With Urgent Budgets Behind ThemA question I often ask PMs I coach:Who loses sleep if this problem is not solved?If the answer is:* “Not sure”* “Eventually the business will feel it”* “It would improve the experience”* “It could move a KPI if adoption increases”Then you do not have a real problem yet.Real product opportunities have:* A user who is blocked from achieving something meaningful* A measurable cost or consequence of inaction* An internal champion with authority to push change* An adjacent workflow that your product can attach to immediately* A budget owner who is willing to pay now, not laterHealthcare translation checks every box. That is why Joe now has institutional adoption and a business with meaningful traction behind it.Why PMs Struggle With This in PracticeIf the lesson seems obvious, why do so many PMs fall into the encouragement trap?The reason is emotional more than analytical.It is uncomfortable to confront the possibility that your idea, feature, roadmap, strategy, or deck is not compelling enough yet. It is easier to seek validation than truth.In my first startup, we kept our product in closed beta for months longer than we should have. We told ourselves we were refining the UX, improving onboarding, solidifying architecture. The real reason, which I only admitted years later, was that I was afraid the product was not good enough. I delayed reality to protect my ego.In product work, speed of invalidation is as important as speed of iteration.If something is not working, you need to know as quickly as possible. The faster you learn, the more shots you get. The best PMs do not fall in love with their solutions. They fall in love with the moments of clarity that allow them to change direction quickly.Actionable Advice for Early and Mid Career PMsBelow are specific behaviors and habits you can put into practice immediately.1. Always test product concepts as choices, not presentationsInstead of asking:“What do you think of this idea?”Ask:“We are deciding between these two approaches. Which one is more important for you right now and why?”This forces prioritization, not politeness.2. Never ship a feature without observing real usage inside the workflowA feature that exists but is not used does not exist.Sit next to users. Watch screen behavior. Listen to their muttering. Ask where they hesitate. And most importantly, observe what they do after they close your product.That is where the real friction lives.3. Always ask: What is the cost of not solving this?If there is no real cost of inaction, the feature will not drive adoption.Impact must be felt, not imagined.4. Look for users with strong emotional urgency, not polite agreementWhen someone says:“This would be helpful.”That is death.When someone says:“I need this and I need it now.”That is life.Find urgency. Design around urgency. Ignore politeness.5. Know the business model of your customer better than they doThis is where many PMs plateau.If you want to be taken seriously by executives, you must understand:* How your customer makes money* What costs they must manage* Which levers influence financial outcomesWhen PMs learn to speak in revenue, cost, and risk instead of features, priorities, and backlog, their influence changes instantly.The Broader Strategic Question: What Happens When Foundational Models Improve?During our conversation, I asked Joe whether the rapid improvement of GPT-like translation will eventually make specialized healthcare translation unnecessary.His answer was pragmatic:“Our goal is to ride the wave. The best technology alone does not win. The integrated solution that solves the real problem wins.” This is another crucial product lesson:* Foundational models are table stakes.* Differentiation comes from workflow integration, specialization, compliance, and trust.* Adoption is driven by reducing operational friction.In other words:In AI-first product strategy, the model is the engine. The workflow is the vehicle. The customer problem is the road.The Future of Product Work: Judgment Over OutputThe world is changing. Tools are accelerating. Capabilities are compounding. But the core skill of product leadership remains the same:Can you tell the difference between signal and noise, urgency and politeness, truth and encouragement?That is judgment.Product management will increasingly become less about writing PRDs or pushing execution and more about identifying the real problem worth solving, framing tradeoffs clearly, and navigating ambiguity with confidence and clarity.The PMs who will thrive in the coming decade are those who learn how to ask better questions.ClosingThis conversation with Joe reminded me that most of the time, product failure is not the result of a bad idea. It is the result of insufficient clarity. The clarity does not come from thinking harder. It comes from testing real choices, with real users, in real workflows, and asking questions that force truth rather than encouragement.If this resonates and you want help sharpening your product judgment, improving your influence with executives, developing clarity in your roadmap, or navigating career transitions, I work 1:1 with a small number of PMs, founders, and product executives.You can learn more at tomleungcoaching.com.OK. Enough pontificating. Let’s ship greatness. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit firesidepm.substack.com

IntroductionOne of the great joys of hosting my Fireside PM podcast is the opportunity to reconnect with people I’ve known for years and go deep into the mechanics of business building. Recently, I sat down with Jason Stoffer, partner at Maveron Capital, a venture firm with a laser focus on consumer companies. Jason and I go way back to my Seattle days, so this was both a reunion and an education. Our conversation turned into a masterclass on scaling consumer businesses, the art of finding moats, and the brutal realities of marketplaces.But beyond the case studies, what stood out were the actionable insights PMs can apply right now. If you’re an early or mid-career product manager in Silicon Valley, there are playbooks here you can borrow—not in theory, but in practice.Jason summed up his approach to analyzing companies like this: “So many founders can get caught in the moment that sometimes it’s best when we’re looking at a new investment to talk about if things go right, what can happen. What would an S-1 or public filing look like? What would the company look like at a big M&A event? And then you work backwards.” That mindset—begin with the end in mind—is as powerful for a product manager shipping features as it is for a VC evaluating billion-dollar bets.In this post, I’ll share:* The key lessons from Jason’s breakdown of Quince and StubHub* How these lessons apply directly to your PM career* Tactical moves you can make to future-proof your trajectory* Reflections on what surprised me most in this conversationAnd along the way, I’ll highlight specific frameworks and examples you can put into action this week.Part 1: Quince and the Power of Supply Chain InnovationWhen Jason first explained Quince’s model, I’ll admit I was skeptical. On its face, it sounds like yet another DTC apparel play. Sell cheap cashmere sweaters online? Compete with incumbents like Theory and Away? It didn’t sound differentiated.Jason disagreed. “Most people know Shein, and Shein was kind of working direct with factories. Quince’s innovation was asking, what do factories in Asia have during certain times of the year? They have excess capacity. Those are the same factories who are making a Theory shirt or an Away bag. Quince went to those factories and said, hey, make product for us, you hold the inventory, we’ll guarantee we’ll sell it.”That’s not a design tweak—it’s a supply chain disruption. Costco built an empire on this principle. TJX did the same. Walmart before them. If you can structurally rewire how goods get to consumers, you’ve got the foundation for a massive business.Lesson for PMs: Sometimes the real innovation isn’t visible in the interface. It’s hidden in the plumbing. As PMs, we often obsess over UI polish, onboarding flows, or feature prioritization. But step back and ask: what’s the equivalent of supply chain disruption in your domain? It might be a new data pipeline, a pricing model, or even a workflow that cuts out three layers of manual steps for your users. Those invisible shifts can unlock outsized value.Jason gave the example of Quince’s $50 cashmere sweater. “Anyone in retail knows that if you’re selling at a 12% gross margin and it’s apparel with returns, you’re making no money on that. What is it? It’s an alternative method of customer acquisition. You hook them with the sweater and sell them everything else.” In other words, they turned a P&L liability into a marketing hack.Actionable move for PMs: Identify your “$50 sweater.” What’s the feature you can offer that might look unprofitable or inconvenient in isolation, but serves as an on-ramp to deeper engagement? Maybe it’s a generous free tier in SaaS, or an intentionally unscalable white-glove onboarding process. Don’t dismiss those just because they don’t scale on day one.Part 2: Moats, Marketing, and Hero SKUsJason emphasized that great retailers pair supply chain execution with marketing innovation. Costco has rotisserie chickens and $2 hot dogs. Quince has $50 cashmere sweaters. These “hero SKUs” create shareable moments and lasting brand associations.“You’re pairing supply chain innovation with marketing innovation, and it’s super effective,” Jason explained.Lesson for PMs: Don’t just think about your feature set—think about your hero feature. What’s the one thing that makes users say, “You have to try this product”? Too often, PM roadmaps are a laundry list of incremental improvements. Instead, design at least one feature that can carry your brand in conversations, tweets, and TikToks. Think about Figma’s multiplayer cursors or Slack’s playful onboarding. These are features that double as marketing.Part 3: StubHub and the Economics of TrustAfter Quince, Jason shifted to a very different case study: StubHub. Here, the lesson wasn’t about supply chain but about moats built on trust, liquidity, and cash flow mechanics.“Customers will pay for certainty even if they hate you,” Jason said. Think about that. StubHub’s fees are infamous. Buyers grumble, sellers grumble. And yet, if you need a Taylor Swift ticket and want to be sure it’s legit, you go to StubHub. That reliability is the moat.Lesson for PMs: Trust is an underrated product feature. In consumer software, this might mean uptime and reliability. In enterprise SaaS, it might mean compliance and security certifications. In AI, it could mean interpretability and guardrails. Don’t underestimate how much people will endure friction if they can be sure you’ll deliver.Jason also pointed out StubHub’s cash flow hack: “StubHub gets money from buyers up front and then pays the sellers later. That’s a beautiful business model. If you create a cash flow cycle where you’re getting the money first and delivering later, you raise a lot less equity and get diluted less.”This is a reminder that product decisions can have financial implications. As PMs, you may not directly set billing cycles, but you can influence monetization models, free trial design, or even refund policies—all of which affect working capital.Actionable move for PMs: Partner with finance. Ask them: what product levers could improve cash conversion cycles? Could prepayment discounts, annual billing, or usage-based pricing reduce working capital strain? Thinking beyond the feature spec makes you more valuable to your company—and accelerates your own career.Part 4: Five Takeaways from StubHub Jason listed five lessons from StubHub:* Trust is a moat – Even if users complain, reliability keeps them loyal.* Liquidity is a moat – Scale compounds, especially in marketplaces.* Cash flow mechanics matter – Payment terms can determine survival.* Tooling locks in supply – Seller-facing tools create stickiness.* Scale itself compounds – Once you’re ahead, momentum carries you.Part 5: What Surprised Me MostAs I listened back to this conversation, two surprises stood out.First, the sheer size of value retail. Jason noted that TJX is worth $157 billion. Burlington, $22 billion. Costco, $418 billion. These aren’t sexy tech names, but they are empires. It made me rethink my assumptions about what “boring” industries can teach us.Second, Jason’s humility about being wrong. “Reddit might be one,” he admitted when I asked about his biggest misses. “I had no idea that LLMs would use their data in a way that would make it incredibly important. I was dead wrong. I said sit on the sidelines.” That candor is refreshing—and a reminder that even seasoned investors get it wrong. The key is to keep learning.Lesson for PMs: Admit your misses. Write them down. Share them. Don’t hide them. Your credibility grows when you own your blind spots and show how you’ve adjusted.Closing ThoughtsTalking with Jason felt like being back in business school—but with sharper edges. These aren’t abstract frameworks. They’re battle-tested strategies from companies that scaled to billions. As PMs, our job isn’t just to ship features. It’s to build businesses. That requires thinking about supply chains, trust, cash flow, and marketing moats.If you found this helpful and want to go deeper, check out Jason’s Substack, Ringing the Bell, where he publishes his case studies. And if you want to level up your own career trajectory, I offer 1:1 executive, career, and product coaching at tomleungcoaching.com.Shape the Future of PMAnd if you haven’t yet, I’d love your input on my Future of Product Management survey. It only takes about 5 minutes, and by filling it out you’ll get early access to the results plus an invitation to a live readout with a panel of top product leaders. The survey explores how AI, team structures, and skill sets are reshaping the PM role for 2026 and beyond.OK. Let’s ship greatness. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit firesidepm.substack.com

From Chaos to Clarity: How AI is Rewriting the Playbook for Product ManagersLessons from my conversation with ex-Google PM Assaf Reifer on building tools that tame the noise, sharpen priorities, and give PMs back their most valuable resource: focus.When I think back on my time at Google, one of the highlights was building and scaling teams with incredibly talented product managers. Some of those PMs went on to lead big initiatives across YouTube, Google Health, and other parts of the company. A few branched out and became founders.One of them is Assaf Reifer, a former PM on my team at YouTube in Zurich. We first met over breakfast through what I think was a LinkedIn networking experiment. He had been at Bain, was exploring his next move, and we happened to be hiring. The match worked out beautifully. He ended up becoming one of the top performers on the team and played a key role in building YouTube Analytics and the transition from the old Creator Studio into what creators now use daily.Recently, I had the chance to catch up with Assaf on my Fireside PM podcast. He’s been experimenting with new projects, one of which could change how PMs everywhere manage the daily chaos of inputs, competing priorities, and distractions. What follows is a long, deep dive into our conversation, plus my take on what early-to-mid career PMs in Silicon Valley can learn from it.The Setup: Why Now Is a Historic Moment for BuildersAssaf started by reflecting on what it feels like to be a builder in 2025. He’s been a software engineer, a consultant, and a PM. But he emphasized that the past two years feel different, historic even.I remarked:“In the last two years with advancements in AI, a lot of the knowledge necessary to build something end to end is really bridged by some of these technologies. It empowers people to realize ideas and experiments that previously required 10 people and millions of dollars.”Think about that for a second. Not long ago, building a SaaS product that could ingest Zoom transcripts, Slack threads, and Jira tickets, then triage them into a priority list for a PM would have required a team of engineers, designers, and product folks. Now a single founder can stitch that together with off-the-shelf AI models, APIs, and some creativity.For early-career PMs, the actionable insight is clear: don’t wait for permission to build. Even if you’re not an engineer, AI has lowered the barrier to entry so much that you can tinker, prototype, and validate ideas faster than ever. Open ChatGPT or Gemini, describe what you want to build, and let the system guide you through the concepts you don’t understand.Assaf encourages this approach:“The best way to start is open ChatGPT or Gemini, tell it what you want to build, and ask it how. It will respond with 30 terms you don’t understand, and you just go one by one. You ask it to explain each concept, and gradually you close the gap very quickly.”That’s the 2025 version of “learning to code.” You don’t need to become a full-stack engineer. But you do need to become fluent in exploring, iterating, and leveraging AI as a co-pilot.The Problem: PMs as Air Traffic ControllersAfter talking about the broader builder landscape, we turned to the problem space Assaf is attacking. We discussed product managers as “air traffic controllers,” juggling multiple channels of information, each with different levels of urgency.“Being a PM is all about prioritizing. You’re interacting with sales, engineering, customers, peers, executives. You have OKRs on one hand, and then Jira tickets or a customer threatening to churn on the other. Until recently, the best PMs just kept it all in their heads or in spreadsheets.”Sound familiar? If you’re a PM, you’ve probably woken up to a wall of Slack notifications, 10 unread emails from sales, and a Jira dashboard full of tickets. Then, by 10am, you’re in a meeting where a senior leader asks, “What do you think about this issue that came up this morning?” And you’re embarrassed because you didn’t even know it existed.I’ve been there. And I bet you have too.The core challenge: noise vs. signal. PMs succeed not because they read every message but because they know which ones matter. That judgment call has historically been a mix of intuition, experience, and luck.The Solution: Issue Center (PM Studio?)Assaf’s project, tentatively called “Issue Center,” is a SaaS tool that ingests all the inputs PMs already swim in: Slack, Jira, Zoom transcript, and applies AI-powered rules to surface the truly critical items.The workflow looks like this:* Integration: Connect the tool to your company’s communication stack. (His design partner is running Microsoft 365/Teams, but it could work with Slack and Google too.)* Rule Setup: Create rules that define what matters to you. For example, “API degradation impacting users” is critical. Or “customer mentions a competitor as better” is high.* AI Assistance: The system uses AI to evaluate whether inputs match your rules. It flags the items, explains why, and links you back to the source.* Prioritized Dashboard: Instead of drowning in messages, you wake up to a curated list of critical, high, and medium issues to tackle first.Assaf demoed it live, showing how rules surfaced relevant Jira tickets, Slack threads, and transcripts. At one point, he laughed at his own naming convention:“Clearly I’m not a marketer. It’s called Issue Center for now, but we can call it PM Studio if that makes it sound cooler.”I told him PM Studio had a nice ring to it.The important thing wasn’t the branding, though—it was the shift from reactive scrambling to proactive clarity.Actionable Takeaway #1: Define Your Own Rules of SignalHere’s where PMs can learn something even before using a tool like this. Ask yourself: What are the true signals in my work?* Is it when a customer threatens to leave?* When an API is degrading?* When an executive brings up a competitor?Whatever they are, write them down. These are your “rules.” Even if you don’t have AI filtering your inputs yet, the discipline of defining rules forces you to separate noise from signal.Assaf admitted that rule-writing is an art:“The rule description is very important, because that’s what the system uses to match. If it’s too narrow, it won’t pick up. If it’s too broad, you’ll get noise. That’s why I want to make onboarding easier with quick-start templates for common rules.”This mirrors how you should think about your own prioritization framework. If you’re too vague (“respond to all customer requests”), you’ll drown. If you’re too narrow (“only focus on API latency under 200ms”), you might miss the forest for the trees.The Bigger Picture: Managers of PMsAssaf also highlighted another layer of value, helping PM leads manage their teams.“If you’re a PM lead and you have a team, you want visibility into what critical topics your PMs care about, what jeopardizes OKRs, and where they need support. This tool can give you that bird’s-eye view.”This is huge. One of the hardest parts of managing PMs is knowing what’s actually keeping them busy. Are they firefighting customer issues? Negotiating with engineering? Or chasing shiny objects?For managers, the actionable advice is: ask your PMs to share their “critical issue list” with you weekly. Even if you don’t have Assaf’s tool yet, that discipline will create alignment and uncover mis-prioritizations.The Privacy Angle: Building TrustWe also talked about the obvious concern: privacy. If your tool is reading Slack messages, Zoom calls, and Jira tickets, where does that data go?Assaf has thought about this deeply:“This is architected as a single-tenant SaaS. It’s installed in your company’s own cloud tenant. Nothing leaves the org. Even when we use AI, it runs through your enterprise API key, which isn’t used for training.”For PMs evaluating AI tools, this is a reminder: always ask how data is handled. At many companies, legal and IT will shut down even the coolest tool if privacy isn’t bulletproof. If you’re the PM championing adoption, anticipate those concerns and come prepared with answers.Actionable Takeaway #2: Trust Is a FeatureIn 2025, building trust is not just about having the right feature set. It’s about handling privacy, security, and reliability as first-class features.If you’re building a product, or even advocating for one inside your company, bake trust into your pitch. Show that you’ve thought about data handling, failure modes, and user control.Beyond Explicit Rules: The Future of Inferred PrioritiesOne of the fun parts of our conversation was brainstorming future features. I suggested that beyond explicit rules, the system could infer priorities by watching behavior:* If you always jump into competitor-related Slack threads, the system could propose a rule.* If you consistently respond faster to certain stakeholders, it could bump their inputs up in priority.Assaf agreed this was interesting but also flagged the risks:“Whenever you do something that isn’t explicitly set by the user and you get it wrong, you risk losing trust. You don’t want noise creeping into the critical bucket.”That’s a broader lesson for PMs: don’t get seduced by complexity if it undermines trust. Sometimes a simple, transparent system is better than a magical one that feels unpredictable.The Side Project: An AI Teddy BearWe spent most of our time on PM Studio, but Assaf also showed me something else: a prototype for an AI-powered plush toy that serves as a conversational buddy for kids.The idea is part educational, part entertaining. Think Teddy Ruxpin meets ChatGPT, but with parental controls and guardrails.He tested it with his own kids, and at one point, a child said he wanted to “eat the squirrel” in a story. The system responded, “That’s not a very nice thing. Let’s try something kinder.”That made me laugh—and also highlighted the importance of building safe AI for children.As a parent myself, I told Assaf:“If this thing could help kids develop critical thinking and curiosity before they jump into ChatGPT, I’d pay money for it. We don’t formally teach critical thinking to children, but a well-designed toy could do it through fun experiences.”While this project is still early, it connects to a broader theme: AI is reshaping not just how we work, but how we learn, parent, and play.Actionable Takeaway #3: Think About Second-Order EffectsFor PMs, the teddy bear might seem irrelevant. But the lesson is this: when you build with AI, think about the second-order effects.* How does this change how people learn, not just how they work?* How does it shape what they trust, not just what they use?* How does it influence long-term skills, not just short-term productivity?If you only optimize for immediate outcomes, you miss the deeper impact your product could have.Practical Advice for PMs in Silicon ValleyLet’s bring this back to you, the early-to-mid career PM navigating the chaos of Silicon Valley. Here are five actionable insights from my conversation with Assaf:* Define Your Critical Rules. Don’t wait for a tool. Write down the signals that truly matter in your role and use them to triage your own work.* Build Trust Through Clarity. Whether you’re building products or pitching ideas internally, make privacy, reliability, and transparency part of your value prop.* Use AI as a Learning Co-Pilot. Open ChatGPT or Gemini and let it teach you the concepts behind the systems you want to build. Don’t be afraid of looking dumb, ask it to explain everything.* Share Priorities with Your Manager. If you manage PMs, ask for their top three critical issues weekly. If you’re managed, proactively share them. It will align expectations and reduce surprises.* Anticipate Second-Order Effects. Don’t just think about what your product does today. Think about how it changes behavior, skills, and trust over time.Why This Matters: The Cambrian Explosion of BuildersWe closed our conversation reflecting on the bigger picture. I remarked:“You wonder if the next hundred billion dollars of market value will come not from 10 decacorns, but from a thousand smaller companies run by 5–10 people. That’s good for customers. It’s good for competition. And it’s possible because of AI.”This is a turning point in product management. The PMs who thrive in the next decade will be those who can harness AI, not just as users, but as builders, integrators, and thinkers.Final ThoughtsCatching up with Assaf reminded me of why I love product management. At its best, it’s about solving messy problems, shaping the future, and helping people focus on what matters most.As you navigate your own PM career, I encourage you to experiment with AI, define your rules of signal, and always keep trust at the core of what you build.And if you want more personalized support, I run a 1:1 executive, career, and product coaching practice at tomleungcoaching.com. If you want to try Assaf’s Issue Center tool as a design partner, feel free to contact him or hit him up on X. OK. Enough pontificating. Let’s get back to work. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit firesidepm.substack.com

We’re back with a Startup Spotlight episode on the Fireside PM podcast. It’s not every day you get to speak with someone who’s straddled the worlds of architecture, gaming, AI, and robotics—and managed to turn those disparate threads into a startup tackling one of the most important problems in our robotic future.Steven Ren, the co-founder and CEO of Palatial, joined me from Lower Manhattan to share the winding journey of his company—from Cornell’s architecture school to optimizing simulations for robot training at scale. We went deep on the technology, market evolution, and product insights he’s picked up along the way—and there are dozens of takeaways here for early and mid-career PMs, especially those building infrastructure, devtools, or working in AI-adjacent spaces.From Watercolors to Headsets: The Early SeedsSteven didn’t grow up dreaming of building tools for humanoid robot training. He actually wanted to be an architect—and studied architecture at Cornell. His turning point came in a multidisciplinary studio class led by Don Greenberg, a legend in computer graphics.“He was always trying to get architects to work together with the CS people… and that really opened my eyes to what immersive tech and real-time rendering could do for communicating spaces.”This interdisciplinary exposure planted the idea that real-time, explorable 3D environments could fundamentally improve how people visualize, design, and collaborate around spaces—both physical and digital.He got a taste of this while at Tesla, working on Giga factory expansion. The rapid pace of construction caused costly design coordination issues, and Steven built a prototype that stitched disparate CAD formats into a fly-through simulation using Unreal Engine.“I put together a pipeline that optimized and converted all the CAD designs into an Unreal Engine level—basically a big game—so they could fly around and see how everything fit together.”It helped prevent expensive errors and even became a tool for internal storytelling. That experience solidified his conviction: digital twins weren’t just cool—they were valuable. He knew he wanted to build a company that scaled that capability.Pivot 1: From Architecture to OptimizationThe initial Palatial concept was ambitious: a cloud platform where architects could upload CAD files and get back interactive, game-like visualizations that clients could explore in the browser.Sounds great—until you realize how unpredictable CAD file structures can be.“Every software is different, and everyone uses the software differently. You have to make foundational translations between how engineers organize a scene and how game engines expect it.”Instead of a tidy black box, they were faced with a combinatorial nightmare of input variability. Worse, customers didn’t want a finished result—they wanted control over how their designs were rendered and experienced.So they pivoted. The new insight: the universal pain point was optimization. Making the scenes look and perform well across platforms.Enter: Palatial as a plugin for Unreal Engine. The new tool became something like “CCleaner for your 3D scene,” scanning for inefficiencies and letting users apply best-practice fixes with a few clicks. Lighting, texture mapping, model merging—all simplified and standardized.“Even if you don’t understand what’s going on, the idea is that you can arrive at a much more optimized project… and sometimes better-looking too.”If you’re a PM shipping developer tools or plugins, take note: this pivot exemplifies how deep user testing can uncover the narrow wedge feature that wins adoption—before expanding.The Aha Moment: Simulations, Not ShowcasesDespite the optimization plugin gaining traction, Steven and the team began to spot a different kind of demand: robotics companies were building millions of virtual environments for training and testing.“You need like hundreds of thousands of environments to teach the robot all the different variations of the world it could come across.”Today, many of those teams manually build 3D scenes—or worse, ask ML engineers to fumble their way through creative tasks. It’s expensive, inconsistent, and distracts from core innovation. Steven saw a gap Palatial was well-suited to fill.So they pivoted again.Now, Palatial is focused on powering massive-scale, high-fidelity simulation environments—starting with objects and scenes that train robots to physically manipulate the real world.PM Takeaway #1: Don’t Fear the Pivot—Engineer for ItMost PMs are taught to avoid scope creep, but what Palatial did is different. They bet on a market’s inevitable evolution (robotics), built a wedge feature (optimization), and used that to find the real platform opportunity (simulation infrastructure).Steven put it plainly:“It’s been a winding journey. We thought we’d serve architects, then realized robot developers had the same need—but at far greater scale.”This is a playbook for product leaders:* Find a general pain point across verticals (in Palatial’s case: messy 3D pipelines)* Build a useful component (e.g., optimization plugin)* Watch for the industry that experiences that pain at 10x scale (robotics vs. architecture)PM Takeaway #2: Build for Openness, Not Lock-InAnother strategic decision: rather than offering a fully walled-off end-to-end platform, Palatial focused on modularity.“We’re going to offer this as an API so teams can build generation into their existing pipeline… and just use that piece.”In a world where AI stacks are increasingly bespoke, trying to own everything can backfire. By being composable, Palatial makes itself easier to adopt—especially for developers already invested in internal tooling.Whether you’re in devtools, AI, or infra, this is a good reminder: great platforms start by being great plugins.PM Takeaway #3: Product-Market Fit Might Be a Who, Not a WhatPalatial didn’t change their core tech—they changed the user.Same backend pipeline. Same rendering engine. But by shifting from architects (low frequency, high customization) to robotics engineers (high frequency, high fidelity), they unlocked a recurring, sticky use case.“We realized this isn’t about showcasing a single building. It’s about training robots through thousands of virtual environments—and those environments need to look and behave like the real world.”This kind of vertical shift is especially relevant in today’s AI world, where many companies sit atop general capabilities. The biggest opportunities often come from narrowing the audience, not the scope.PM Takeaway #4: Speed is the New MoatIn one of my favorite moments, I asked Steven how he thinks about competitive defensibility.His answer:“There’s no such thing as a technological moat anymore. The moat is speed—having a nimble team that can iterate fast and adapt.”We’ve heard echoes of this across the startup world, but it hits especially hard in AI and frontier tech. If you’re leading a PM team, ask yourself: are you shipping faster than your competitors can copy you?And if not, why not?PM Takeaway #5: Accuracy Will Be the Differentiator in the Robot EraOne thing Steven emphasized again and again was realism. In order for simulation-trained robots to be effective, their environments must behave like the real world. That means physical properties, lighting conditions, and object metadata all matter.“There’s no point in generating data if it doesn’t match reality. You can generate as much crappy data as you want—it’s like oversweetened candy. You don’t want it.”In other words: in the age of synthetic data and generative tools, quality—not just quantity—will win.As a PM, that might mean:* Prioritizing fidelity over speed when the stakes are high* Partnering with domain experts to tune your models* Making room for manual curation and validation—even if it slows you downPM Takeaway #6: Be Willing to Outgrow Your Initial MarketSteven was candid about the limits of their original architecture play:“It was kind of a one-and-done thing. There’s a bigger market where you need many environments, all the time.”This highlights something I often tell coaching clients: your first ICP (ideal customer profile) is often just a foothold. Pay attention when your usage data, pricing power, or support requests point to higher-value customers in adjacent markets.Where Palatial Is HeadedToday, Palatial is in the middle of rolling out their MVP for simulation-ready 3D asset generation. These aren’t just pretty models—they contain metadata about mass, bounce, physics, and more, making them usable for training and validation.They’re also building the tooling to generate full environments from those assets and optimize them for scale.Eventually, Steven sees a future where the robots themselves are capturing and syncing environments in real-time:“Eventually this will be onboard the robots. As they walk around, they’ll translate what they see into a digital twin—and train on that in the background.”That vision is a long way off. But Palatial is betting that when we get there, infrastructure like theirs will be indispensable.Final ThoughtsIf you’re an early or mid-career PM, a few questions to reflect on:* What new verticals are quietly developing the same problems my team is already solving?* Is there a simpler, standalone piece of my product that could become a wedge?* Am I over-investing in platform scope vs. developer modularity?* Is my team fast enough to stay ahead in a post-moat world?If you want to stay close to the frontlines of robotics infrastructure—or you just want to learn from a founder iterating in public—follow Steven Ren and check out palatialxr.com.And if your own company is navigating complex product strategy decisions or early-stage growth hurdles, I offer one-on-one coaching at tomleungcoaching.com, and product consulting and startup advisory services at paloaltofoundry.com.OK, enough pontificating. Back to work, team. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit firesidepm.substack.com

“We Are Not in Kansas (or Creatorland) Anymore”When I kicked off this Fireside PM interview with Ben Grubbs, I knew we’d cover the creator economy. What I didn’t expect was how much of it would end up being an MBA seminar for product managers.Ben isn’t just another ex-YouTube guy with creator war stories. He’s seen the evolution of the online video ecosystem from its scrappy, quirky beginnings to the billion-dollar global marketplace it is today. His vantage point spans across YouTube FanFest, the launch of YouTube Kids, and later, his own venture Creator+.But this isn’t a nostalgia trip. This conversation is about understanding where the creator economy went right, where it went off the rails, and what PMs and builders can learn from those who survived—and thrived.Let’s break it down.1. Don’t Just Sell Picks and Shovels—Sell Gold Bars TooThere’s an old startup trope: during a gold rush, the people who make the money are the ones selling picks and shovels.Ben and I reflected on this assumption when it came to the 2021–2022 wave of creator economy startups—tools for analytics, monetization, editing, payroll, and more.A lot of those bets fizzled.Why?Because the “miners”—the creators—were not your typical enterprise buyers. Most didn’t make enough to justify expensive tools, and those who did weren’t being well-served.“You had companies working with hundreds or thousands of creators,” Ben said. “But they were all Tier 5 or Tier 6. The top creators—the ones running real businesses—weren’t touching these tools. The startups couldn’t crack that ceiling.”Creators with scale (think Tier 1) needed tools built with deep empathy for their workflows—but often the tool builders didn’t even have relationships with these creators.It’s a warning for PMs: Just because there’s a problem doesn’t mean the solution is a venture-scale business.Ben would often gut-check startup ideas by calling former colleagues at YouTube to ask if the feature in question was in the product roadmap.“If they told me it was far down the list—great. That’s a two-year runway. But if it was near the top? I’d pass.”Takeaway for PMs: Before betting your career or company on a “picks and shovels” play, ask:* Can I serve the high-value users, or am I stuck with long-tail?* Is this something the platform will inevitably build?* Does this idea have cross-platform defensibility?If the answer to all three is “no,” it’s probably not a durable business.2. The Myth of the Accidental CreatorOne of the most common origin stories in the creator economy is the passionate hobbyist who stumbled into success. But that’s no longer the only model—or even the dominant one.Ben contrasted the early YouTube generation with today’s operator-led brands like Good Good Golf, where content wasn’t the product—it was the acquisition channel.“This wasn’t some happy accident. Good Good had a clear business strategy from Day One. Content was the top-of-funnel. They were always going to build a real consumer business.”And build they did. Good Good went from viral YouTube content to a thriving golf apparel and equipment brand, all while keeping production margins high and paid marketing spend low.How? They applied DTC logic to a creator-native model. Instead of paying for reach, YouTube paid them to market their own products.“Some DTC founders were stunned by their margins. But they didn’t realize: Good Good gets paid for their marketing.”Ben’s point: this isn’t selling out. It’s growing up.And it’s working.Actionable Tip for PMs: When evaluating growth loops, ask yourself:* Is our content serving a bigger business objective?* Can our audience also become customers?* Are we building a brand—or just renting attention?3. Build for the Power LawWe all know the creator economy is a power-law business. But what does that mean for those building around it?Ben shared a fascinating stat from his YouTube days: at one point, 4,500 creators met the threshold to qualify for top-tier partnership. But YouTube had resources to serve just 500.“We couldn’t support everyone. And the people who qualified were far more than we could manage. That’s when I realized: there's a huge gap.”That gap created opportunities—but only if you could build for the whales.Most of the SaaS tools went after the long tail. Wrong call.“The top creators are basically SMBs. They need operational support, yes—but they also need defensible strategy, content licensing, IP management. That’s not just software—that’s consulting, services, and deal-making.”Moonbug is a perfect example.They weren’t a tool. They were a studio that centralized production, built IP (like Cocomelon), and sold toys, media rights, and more. They exited for over a billion dollars.For PMs and founders, the takeaway is this:* Don’t assume the long tail is the market.* Go upstream. Serve the whales.* Focus on full-stack solutions, not just utilities.If you’re not building something worth $10M+ in ARR from a dozen clients, you're probably building a feature, not a business.4. MrBeast Is a Company, Not Just a Creator—and That’s the PointWe couldn’t have this convo without talking about MrBeast.Ben sees Jimmy Donaldson as a pioneer not just in content, but in company structure. His organization isn't a hobbyist’s shop—it’s a holding company with a real CEO.“Jimmy’s not the CEO. He’s the chairman. They hired a real operator from public markets. That person is building a corporate org. They’re hiring institutional people. It’s becoming a conglomerate.”Unlike most creator ventures—where investors buy into just one slice of the pie—MrBeast’s holding company gives investors exposure to all ventures.Think Alphabet, not a side hustle.“It’s better alignment. If you’re putting in capital, you want access to the whole thing—not just the candy bar business or the mobile game.”And that model might just be repeatable.As Ben put it: “A lot of creators say they want to be CEOs. But once they see what CEOs actually do—HR, legal, compliance—they change their mind fast.”Jimmy didn’t want to be bogged down in operations. So he hired someone who could be.PM Insight: If you're working with high-talent individuals—creators, researchers, engineers—don’t just elevate them to management. Design orgs where they can focus on their strengths and bring in ops leaders to scale.5. AI Is Not the End. It’s the Efficiency Revolution.Toward the end of the episode, we dove into AI’s impact on the creator economy.Ben doesn’t see it as a doomsday scenario. Quite the opposite.“One animation company showed me a tool that turned a sketch into a production-ready 3D model—in real time. That’s insane. The question is: do you lower your prices… or do you double your margin?”That’s the rub.AI will reduce production costs. Which means more creators will have studio-grade tools at their fingertips. It also means fewer people per production.“I was on a shoot with 50 people. Half weren’t doing anything. I realized the producer brought them in for optics—to make it look big-budget.”In other words, there’s fat to be trimmed. And AI is the scalpel.I brought up the stability of the power-law impact. The best AI-assisted content will still win. Most will get buried.“It’s like CGI in the movies. People feared it would kill cinema. Instead, it became standard. AI might be the same—just another tool.”For your PM roadmap, this means:* Expect higher expectations from users.* Deliver faster, smarter workflows.* Don’t fight AI—integrate it.TL;DR: Actionable Advice for PMs in Silicon ValleyHere are the five key lessons from my talk with Ben Grubbs that every PM should remember:1. Validate Against the Platform’s RoadmapBefore building around YouTube, TikTok, or Instagram, ask: Is this 12 months from being native? If yes, pivot.2. Serve the Top of the PyramidThe most successful creators need full-stack services and strategic guidance—not basic tools.3. Build Brand, Not Just ProductCreators who win big start with brand ambition, not content luck. Align your product roadmap accordingly.4. Separate Creator Talent from CEO SkillsetsGreat creators aren’t always great operators. Your org chart needs to reflect that.5. Use AI to Win on EfficiencyAI won’t replace you—but the PM who uses AI will. Bake it into production and product from the ground up.Final Thoughts: Betting on the Right Side of DisruptionAs Ben told me:“You want to be on the side of the disruptor. Not waiting to get disrupted.”That’s never been truer than in 2025. Whether you're working at a Big Tech platform, building the next venture-backed app, or leading product at a creator startup—this space is still changing fast so go where the puck is going and realize that puck is leaping ahead every month.If you're navigating a challenging PM role or trying to make your next career move in tech, I offer one-on-one coaching through TomLeungCoaching.com. For companies that want to accelerate their product strategy or AI roadmap, check out my advisory work at PaloAltoFoundry.com.OK, enough pontificating. Let’s get back to work. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit firesidepm.substack.com

Hey Team,In the latest episode of the Fireside PM podcast, I had the pleasure of chatting with Jake McKee—one of the early advocates of modern community strategy in tech. Jake’s résumé is legit: he’s helped companies like Lego, Apple, and Southwest Airlines transform how they engage their most passionate users—not just as customers, but as collaborators. We covered a ton of ground, and I left the conversation with one overwhelming takeaway:"Features can be copied. Community relationships are the true moat."Jake didn’t say that verbatim—but he may as well have. If you're an early- or mid-career product manager trying to build something people will love, advocate for, and stick with, then keep reading. This post is packed with practical advice, examples, and yes—plenty of quotes—to help you rethink how you build products.From Plastic Bricks to Passionate BuildersLet’s start with Lego. Jake joined the company during a time when the 18+ fan base—the adults building elaborate train sets and sculptures—was considered almost irrelevant. Lego was laser-focused on their core audience: boys ages 7–12.Jake recalled:“They weren’t even considered a segment, let alone a priority. When I joined, the adult fans made up 1% of the business. Today it’s closer to 45%.”How did that shift happen?Jake and his team didn't just ask adult fans to buy more products—they encouraged them to share their creations, hold public exhibits, and advocate for Lego in the real world. And they did it for free. These superusers weren’t incentivized by checks; they were driven by passion. Jake simply gave them tools and encouragement. He even coached them on things like how to invite media to their events or partner with local retailers for promotions.“I was whispering in their ear—‘Have you ever thought about getting the media here? Handing out coupons?’ That kind of thing. I was a connector.”For PMs, the lesson is simple: sometimes the most impactful growth strategy isn’t a new feature—it’s unlocking what your users already want to do.Community Development ≠ Social Media ManagementBefore we go further, let’s get clear on what Jake means by “community.”“It’s the formal and informal, direct and indirect ways to connect the company with customers in a way that leads to shared positivity for both sides.”This isn’t about launching a Discord or running a Twitter account. It’s about building systems of feedback, advocacy, and co-creation—structures that allow customers to influence product development, feel heard, and ultimately take pride in your company’s success.And it doesn’t always require a fancy platform. It might look like a customer advisory board. A monthly AMA with your PMs. A product preview group of superusers giving you feedback at the 75% build stage.It also doesn’t require massive budgets.“I'd much rather give somebody a T-shirt that has the program name on it—something we came up with together—than a $100 gift card. The card gets forgotten. The T-shirt gets worn for years.”That example came from his time at Apple, where they created custom luggage tags for top contributors in the support forums. The packaging was signature Apple. The note inside? “Thank you for being on this journey with us.” No gimmicks. Just gratitude.Community-Driven Product Development: The FrameworkJake has developed a system he calls Community-Driven Product Development (CDPD). It’s a four-part framework that any product team can apply:* Find the Right PeopleNot just your loudest users, but a cross-section of your audience: new users, power users, skeptics, experts, novices. Diversity isn’t just about demographics—it’s about experience and perspective.* Right TimingInvolve users at the right phase of the product cycle. During ideation, you might want 3–4 brainstorming partners. At the 75% mark, you might have 40 people test real workflows. Jake stressed the importance of moving beyond “transactional” feedback loops, like one-off surveys, toward relational ones that evolve over time.* Define OutcomesBe clear on what you’re trying to learn. Is it usability? Emotional resonance? Feature clarity? Align your engagement format with your research questions.* Design the Right ActivitiesMake participation meaningful and rewarding—not necessarily with money, but with access, voice, and recognition.“The most joy we saw was when our users were talking directly to product managers. And funny enough, the PMs got more energized too. It made the work feel like it mattered again.”Advice for Silicon Valley PMsJake’s message to product managers is blunt: You can’t outsource community to marketing. You can’t delegate empathy to a survey.“I always say: What’s the ROI of a conversation? Of a relationship? You can’t calculate it with a spreadsheet. But you feel it when it’s gone.”If you're in the middle of building something, consider these tactical shifts:* Invite users early. Don’t wait until beta to get feedback. Build relationships during ideation and prototype stages.* Create champions. Identify customers who already love your product and ask them to be part of a long-term council or program.* Think about connection, not control. Your job isn’t to “manage” the community—it’s to help the company be more transparent, accessible, and human.* Narrate the journey. Share the “why” behind roadmap decisions. Let users see the people behind the product.Overcoming Internal ResistanceJake told a story from his Lego days that really stuck with me. Early on, no one in marketing would accept his meeting invites. So he stood outside their offices and waited for their meetings to end—then slipped in for a five-minute chat.“That happened enough times that they figured, ‘He’s gonna talk to me anyway—I may as well put him on the calendar.’”The point? Start small. Be persistent. Show the value through stories, not just decks. Find one internal ally who “gets it” and help them shine.He also emphasized starting where you are.“Do something small that you can grow. A T-shirt. A call. A visit. That’s your leverage.”The Value of Real RelationshipsOne of my favorite moments from our chat was when Jake described the joy his scale modeling friends bring him compared to showing his creations to his family.“My family says, ‘That’s cool.’ But my friends? They ask, ‘How did you make that cut? What glue did you use?’ They get it. They care.”That’s the energy you want between your product team and your users. Not applause. Understanding.In a world where PMs are drowning in dashboards and AI-generated summaries, that kind of emotional signal is rare—and priceless.My Own Experience: Two Projects, Two OutcomesTalking to Jake made me reflect on two product launches I’ve led in the past decade.In one, we had users in the office every month. We built trust. We showed them early wireframes. We even debated scope and direction together. That product launched smoothly and hit its adoption goals within months.In another, we did a lot of user research—but it was more transactional. Surveys. Interviews. Data. There was a clear wall between us and the users. The product eventually found its footing, but it took longer and didn’t inspire the same loyalty.Why? Because in the first example, our users were co-conspirators. In the second, they were “subjects.”Why This Matters NowJake and I talked about AI, and how it’s automating more and more of our day-to-day work. The question becomes: What do we do with the time we save?“We’re entering a new phase of creative culture. Hobby and craft are more respected now than ever. The next gen gets that.”That applies to users—but also to us. Community work isn’t some soft, squishy side project. It’s how you future-proof your product and energize your team.“Nobody is loyal to a brand. They’re loyal to a belief, an experience, a person. If you want loyalty, build those.”TL;DR: Takeaways for PMsHere’s your tactical cheat sheet:✅ Start early. Bring users into the conversation before the roadmap is locked.✅ Build relationships. Find 5–10 users who are willing to go deeper than surveys.✅ Share your why. Don’t just ask for feedback—share your constraints and goals.✅ Create artifacts. T-shirts, notes, community-only events—they go further than money.✅ Help your PMs feel human. Let them talk to real users. It will energize the team.✅ Use community to de-risk launches. Your best advocates are built before launch day.✅ Make it sustainable. One-time research projects are fine, but community is a flywheel.Learn more about Jake here.If you found this valuable and want to build a more community-driven product culture at your company, I’d love to help.👉 For product leadership coaching: tomleungcoaching.com👉 For fractional CPO and consulting work: paloaltofoundry.comLet’s build products people care about.Aright, enough talking. Let’s get back to work! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit firesidepm.substack.com

Early to mid-career product managers in Silicon Valley often dream of landing the perfect job—high comp, strong team, great product, and a clear path for impact. But what if I told you that sometimes the smartest move is walking away?That was the case for Elizabeth Hague, a seasoned marketing leader with two successful exits under her belt. She recently turned down a VP of Marketing role that came with a $350K offer, and her reasoning provides invaluable lessons for navigating today's brutal job market.This post is packed with her insights, specific examples, and actionable advice to help you avoid career missteps and make smarter choices as you navigate your next big move.Lesson 1: "You Have to Market Yourself"One of the first points Elizabeth made in our conversation was how ironic it is that even experienced marketers struggle to market themselves. And that’s true for product managers, too. The hiring market is not what it was in 2017, when smart generalists could land jobs with strong interview skills and broad expertise. Today, hiring managers want hyper-specific, battle-tested experience in their exact industry and problem space."It's an employer’s market right now, especially in B2B SaaS. Any small thing can be a reason for rejection," Elizabeth said. "If you're not putting real effort into marketing yourself, refining your personal brand, and strategically positioning your experience, you're already behind."Takeaway: If you're on the job hunt, think about how you’re presenting your skills. Are you crafting your story in a way that aligns with what hiring managers are actually looking for? Use data, case studies, and specific examples to sell your impact—not just a list of job titles.Lesson 2: Beware the "Cinderella Fit" TrapElizabeth and I both noted how today’s hiring market is much more rigid than in past years. "This is not a market where a hiring manager says, 'Oh, this person is smart and driven, they can figure it out,'" I said. "They want someone who has done this exact job before, maybe even at a bigger scale. They can afford to be that picky."This is why so many PMs struggle to break into new domains or level up into leadership roles. Companies often have 500+ applicants per job, so they optimize for the path of least resistance—hiring the safest, most obvious choice.Takeaway: If you’re trying to make a jump—whether it’s into leadership, a new industry, or a new function—you need to be strategic. Build bridges, seek out internal opportunities to gain experience before you switch, and cultivate relationships with decision-makers who can vouch for you.Lesson 3: How to Spot Red Flags in Job OffersElizabeth’s experience turning down a VP job was a masterclass in knowing when to walk away. She identified multiple red flags in her interview process:* Unrealistic Growth Goals: The company expected to 10X revenue in 12 months but had no product marketing team, no demand gen, and had shut off all paid ads.* Underinvestment in Key Functions: The entire marketing budget—including headcount—was just $1M.* High Turnover: The previous VP was fired, and the team was described as "low performers." That’s often code for "leadership doesn’t know how to support and develop talent."* CEO With a Misaligned Vision: "When I asked if these aggressive goals came from the board or him, he said they were his own," Elizabeth noted. That suggested an executive with unchecked expectations."If I didn’t have my internal list of non-negotiables, I might have ignored these signs and taken the job," she admitted. "It’s really easy to rationalize a risky decision when you're in the moment."Takeaway: Before you take an offer, do your diligence. Ask about resourcing, past performance, and leadership expectations. If the math doesn’t add up, trust your gut.Lesson 4: The "Honeymoon Discount" and Why You Should Apply ItWhenever I coach product managers on career decisions, I recommend applying a 30% honeymoon discount—whatever you think the job is, assume it’s at least 30% harder, messier, and more dysfunctional than it appears."No matter how much diligence you do, there will always be surprises once you're inside," I said in our discussion. "And I have never seen a situation where a job turns out to be better than expected."Takeaway: When evaluating an offer, don’t assume best-case scenarios. Consider worst-case risks and be sure you’re comfortable with them before signing on.Lesson 5: When It’s Okay to Take a "Less Than Ideal" JobNot everyone has the luxury of turning down offers. Some people need to get back in the game, rebuild confidence, or simply pay the bills.Elizabeth acknowledged this, saying: "I got a few angry comments on my LinkedIn post—people saying, 'Must be nice to turn down that money!' And I get it. But I wasn’t willing to sacrifice my health and sanity for a role I knew was set up to fail."I also noted that some people take jobs just to “ride the cow while looking for the horse” (an old Cantonese saying). Some PMs strategically take an imperfect job to get back in the market while continuing their search."It’s a totally valid approach, as long as you go in eyes wide open and set your own expectations accordingly," she said.Takeaway: There’s no shame in taking a suboptimal job if it meets an immediate need. Just be clear on your own boundaries and don’t let short-term survival mode dictate long-term career decisions.Final Thoughts: The Career Playbook for Today’s PMsThe job market today is tougher than it’s been in years. But that doesn’t mean you should settle for roles that will burn you out or set you up for failure.Key takeaways:✔ Market yourself strategically—don't assume your resume speaks for itself.✔ Be aware of how the hiring market has changed—"Cinderella Fit" roles dominate.✔ Spot red flags early—don’t wait until you’re six months in to realize the job is a disaster.✔ Apply the "honeymoon discount"—assume every job will be harder than it looks.✔ If you must take a less-than-perfect job, do it intentionally and keep your options open.If you're a product leader looking for 1:1 career coaching, check out TomLeungCoaching.com. And if you’re at a startup needing product strategy consulting, visit PaloAltoFoundry.com.Follow Elizabeth on LinkedIn.As always—let’s get back to work. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit firesidepm.substack.com

All right, what's going on, team? We are back on the Fireside PM podcast, and today I want to share some hard-earned insights on applying to tech startup accelerators. I just finished reviewing over 80 applications for UC Berkeley SkyDeck and Stanford GSB's summer entrepreneurship programs, and I have a ton of thoughts on what makes applications stand out—and what sends them straight to the 'no' pile.If you're a founder looking to get into one of these programs (or even just raise pre-seed money), listen up. Because after reading and rating all these applications, I’ve spotted clear patterns in what works and what doesn’t.Why This MattersWhether it’s Y Combinator, Techstars, or a university-backed accelerator, getting into a top program can significantly change your startup’s trajectory. It’s not just about funding; it’s about credibility, mentorship, and an alumni network that can open doors. But the competition is fierce, and most applications don’t make the cut.I’m sharing my perspective not as an official spokesperson for these programs, but as someone who has been on the selection committees. These insights can give you a better shot at getting accepted—or at least prevent you from making rookie mistakes.The Harsh Reality: Most Applications Are MediocreThe first thing that struck me was how many applications were painfully generic. "We are building AI-powered solutions for X." Great. So is everyone else. The reality is:"Most applications don’t stand out because they don’t make me believe this team is the one to solve this problem."The best applications convinced me that the founders deeply understood the problem, had unique insights, and were doing something difficult yet compelling.1. Show, Don’t Tell: Your Idea Is Not EnoughA huge mistake I saw was founders assuming their idea alone was enough. Just having an idea—even a great one—isn't a differentiator. Execution and traction matter."If you’re pre-product and pre-revenue, you better have a crazy impressive background or some early traction that proves you’re not just another person with a PowerPoint."Some founders just threw in a generic problem statement and a solution slide without showing any proof that they could execute. The best applications showed:* Early customer interest (waitlists, LOIs, pre-sales)* Prototypes or MVPs* Unique industry insights that others don’t haveOne application that stood out came from a founder who had already hacked together an MVP and had 100 users testing it. Another had letters of intent from two Fortune 500 companies. Those got a second look.2. Make Your Founder Story Work for YouEvery founder has a story, but not all stories are compelling. A strong application makes it clear why you are uniquely suited to solve this problem."The best applications make me think: ‘Of course this person should be doing this startup.’"If your background doesn’t directly tie to your startup, find a way to make it relevant. Maybe you’ve spent 10 years in the industry and have insights others don’t. Maybe you built something similar before. Maybe you have an unfair advantage in distribution. Whatever it is, highlight it.Weak applications left me wondering: why this person? Why now? If I can’t answer that, you’re in trouble.3. Specificity Wins: Avoid the ‘AI-Powered’ TrapA major turn-off was vague, buzzword-heavy descriptions. If your pitch is "We use AI to optimize X," without specifics, it’s a red flag. AI is a tool, not a strategy. What exactly are you doing that others aren’t?One founder wrote:"We use AI to improve customer service experiences."That’s meaningless. Compare that to:"Our AI-driven chatbot for e-commerce brands has reduced support ticket volume by 37% in our pilot with three Shopify stores."The second one gets my attention.4. Big Market? Show Your MathMost applications claim they’re tackling a multi-billion-dollar market, but few show how they get there. The best applications broke it down:* TAM (Total Addressable Market): The total demand if everyone in the world used your product* SAM (Serviceable Available Market): The segment you realistically reach with your distribution model* SOM (Serviceable Obtainable Market): The share you can actually capture in the next 3-5 years"If you just throw a $10B market size number without context, I assume you’re making it up."Show your math, cite real sources, and make me believe your assumptions.5. The Team Section Can Make or Break YouSome of the strongest applications had killer teams. Not just impressive resumes, but complementary skill sets that made sense together."A red flag is when it’s all business folks and no one technical, or vice versa."One startup had two MBAs and no engineer. Another had four engineers but no one who had ever sold anything. That’s a tough sell. If you have a gap, acknowledge it and explain how you’ll fill it.The Applications That Got a ‘Yes’ From MeWhile most applications were forgettable, a few stood out. Here’s why:* Traction: Even a tiny bit of real-world validation (a waitlist, a pilot customer, an MVP) made a difference.* Deep market understanding: They articulated why now and why them with clarity.* Clear problem-solution fit: They explained the pain point in a way that made me nod in agreement.* Compelling team: The founders had unique experience or skills that made them credible.Final Thoughts: Get the Basics RightI get it—applying to accelerators is tough, and competition is brutal. But too many founders shoot themselves in the foot by submitting weak applications."If your startup idea doesn’t feel inevitable after reading your application, you’re not ready."Before you hit submit, ask yourself:* Would an outsider instantly understand why this idea must exist?* Does my traction (or background) de-risk my ability to execute?* Is my founder story compelling?* Have I removed all jargon and made my pitch crystal clear?If the answer isn’t a strong "yes" to all four, keep refining.And if you want personalized guidance on your product or startup strategy, check out my 1:1 coaching practice at tomleungcoaching.com and my consulting work at paloaltofoundry.com.Let’s get back to work. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit firesidepm.substack.com

AI is transforming product management, but not everyone is using it effectively. Too many product managers treat AI as a glorified autocomplete—something that speeds up basic tasks but doesn’t fundamentally change their workflow. But after my conversation with Mustafa Kapatiya, a former Google executive and AI consultant, I walked away convinced that the best PMs aren’t just using AI—they’re redefining their entire approach around it.Mustafa has been deep in the trenches, helping product teams harness AI to maximize efficiency and impact. He shared insights from working with organizations that are getting it right—and from those that are stuck at the surface level, frustrated that AI "just doesn’t get it." The difference? Knowing how to ask the right questions, structure inputs properly, and train AI to think like your best-performing team members.Why Most PMs Are Stuck in the Shallow EndMost product managers start with AI the same way they started using Google Docs or Jira—it’s a tool, not a game-changer. They pop open ChatGPT or Claude, ask for a quick summary, maybe a user story template, and move on. Mustafa has seen this pattern again and again:“Most PMs use AI in a very surface-level way. They play around with ChatGPT, get some decent results, but then get frustrated when AI ‘doesn’t get it.’ The reality is, they’re not using even 20% of what AI can actually do for them.”The key distinction between elite PMs and the rest? Elite PMs don’t just ask AI for one-off answers. They integrate it deeply into their workflow. They train it to understand their company’s OKRs, their team’s strengths and weaknesses, and the nuances of how decisions get made.“Top PMs think about AI in three key dimensions: speed, quality, and effort. They don’t just use it to go faster—they use it to produce better work and to minimize the time they personally spend on low-leverage tasks.”AI as Your Second BrainImagine you’re a PM trying to make sense of customer feedback from hundreds of app reviews. The average PM might copy-paste a few into ChatGPT and ask for sentiment analysis. The great PM, on the other hand, does something entirely different. They:* Train AI on their past decisions, OKRs, and company priorities.* Give AI structured data and ask for a synthesized, weighted summary.* Use AI to determine which insights actually move the needle, rather than just producing a generic report.“If you ask AI the wrong question, you get a generic response. If you train it on your context, give it structured inputs, and refine its responses, it becomes a true second brain. That’s where the magic happens.”Mustafa demonstrated this in real time during our chat. He uploaded raw Figma app reviews into Claude, structured a thoughtful prompt, and within minutes, AI produced a highly actionable summary: key pain points, frequently requested features, and a breakdown of sentiment trends. But he didn’t stop there. Instead of just handing that report off to stakeholders, he used AI to determine which insights mapped to team OKRs and who on his team needed to see them first.This is where AI becomes more than a speed tool—it becomes a decision-making engine.The New Playbook for AI-Powered PMsSo how do you move from surface-level AI use to best-in-class execution? Mustafa outlined three core shifts that separate the best from the rest:1. Write Better Prompts (And Reuse Them)Most PMs write one-off prompts each time they need AI to do something. The best PMs treat prompts like reusable assets, refining and improving them over time. Mustafa has even built a PM Playbook—a collection of prompts that cover everything from user research to roadmap prioritization.“You should never be writing the same AI prompt from scratch every time. Write it once, refine it, and reuse it. A great prompt is like a great framework—it saves you hours every week.”2. Train AI on Your ContextIf AI doesn’t understand your company, your team, and your unique challenges, it’s going to spit out generic advice. The best PMs create “digital twins”—structured datasets that teach AI about their org structure, key stakeholders, and product priorities.“I train my AI coach on five key dimensions: company context, my role, team structure, product specifics, and my strengths/weaknesses. This allows AI to give me insights that feel deeply relevant, rather than just surface-level observations.”3. Use AI to Navigate Organizational ComplexityPMs don’t just build products—they navigate company politics, stakeholder expectations, and resource constraints. AI can be a powerful tool for this. Mustafa showed how he uses AI to analyze who in his org is most likely to support or resist a given initiative, helping him craft better pitches and build alignment faster.“One of my favorite use cases? AI as a political coach. It helps me figure out how to frame conversations, who to bring in early, and how to avoid roadblocks before they happen.”The Future: Leaner, Smarter Product TeamsAI isn’t just changing how PMs work—it’s changing who gets hired and how teams are structured. I shared a hypothesis with Mustafa: what if, in the near future, a small team of elite PMs, deeply trained in AI, could outperform a traditionally structured product org with layers of GPMs, APMs, and specialists?Mustafa agreed—and took it a step further:“I actually think we’re about to see a huge shift in how product teams are structured. Instead of large teams with lots of layers, we’ll see small groups of highly effective PMs, each managing multiple products with AI as their co-pilot.”If this happens, the role of the PM will evolve. Instead of spending 80% of their time on documentation, synthesis, and reporting, PMs will focus on decision-making, strategy, and creativity. AI will handle the grunt work—PMs will steer the ship.What This Means for YouIf you’re a PM today, this shift is already happening. The question is: are you ahead of the curve, or are you still treating AI like a toy?Here’s what you can do today:* Start treating AI as a second brain, not a search engine. Train it, refine it, and make it work for you.* Build your own AI playbook. Save and refine the prompts that work. Share them with your team.* Use AI to navigate your organization, not just write documents. It can help you build alignment, anticipate roadblocks, and move faster.Mustafa and I barely scratched the surface in our conversation, but one thing was clear: the PMs who master AI will have an unfair advantage. The ones who ignore it? They’ll be left behind.If you’re serious about leveling up, I highly recommend checking out Mustafa’s AI playbook and following his work on Substack and LinkedIn. And if you want to get direct coaching on AI-driven product management, feel free to reach out.The future belongs to PMs who know how to work with AI. Are you ready?Mustafa’s Free Assessment (limited seats): https://bit.ly/4kesaVjMustafa’s Linkedin: https://www.linkedin.com/in/kapadiamustafa/Tom’s Coaching Page This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit firesidepm.substack.com

Alright, team—we are back on the Fireside PM podcast, and this time, I had the privilege of speaking with Dave Lu, a veteran in tech and one of the most influential voices in the AAPI founder ecosystem. Dave’s career spans Yahoo, Apple, eBay, startups, and venture capital, and in our conversation, he shared his journey and insights on breaking barriers, fostering a strong AAPI network, and navigating leadership as an Asian American in tech.This was a fascinating discussion, and I want to highlight some key takeaways, especially for product managers, startup founders, and anyone looking to advance their career in tech while navigating systemic challenges.From Consultant to PM to Founder: Dave's JourneyDave's path started with finance at Wharton, followed by a brief stint in consulting before realizing it wasn’t for him. He pivoted into product management at Yahoo Finance, back when Yahoo was still under 1,000 employees and Jerry Yang sat just around the corner.“It was probably one of the best jobs I’ve ever had. The team that was there went on to do amazing things.”After Yahoo, personal circumstances led Dave back to New York, where he joined Sony before heading to Stanford GSB for his MBA. From there, he worked at Apple and eBay in product strategy before taking a big leap into startups. His ventures included FanPop, a consumer community platform, and Paired, a labor marketplace for restaurants.“I realized early on that I needed to build something of my own. I didn’t want to wait for someone to promote me—I wanted to take control of my career.”The Power of an AAPI Founder NetworkWhile running his startups, Dave noticed a fragmentation in the AAPI founder community. Unlike other groups—Jewish founders, South Asian entrepreneurs—there wasn’t a strong network for East Asian and Southeast Asian founders. So, in 2011, he started an informal dinner group with eight founders. That group eventually grew to 200+ members, including the likes of Tony Xu (DoorDash) and Eric Yuan (Zoom).“I’m a firm believer that without a strong network, you can’t succeed. That’s how you meet investors, hire talent, and find great advisors.”When the pandemic hit, many AAPI founders struggled to raise capital. Dave decided to help by syndicating a few angel investments via AngelList. What started as a plan to fund a few founders turned into Hyphen Capital, a movement that raised $30 million for 90+ startups, with over 50% of them led by women—far exceeding the industry’s typical 2% funding allocation for female founders.“Rather than banging our heads against the bamboo ceiling, why don’t we just build our own houses?”Overcoming the Bamboo CeilingWe dug into the bamboo ceiling—the systemic barriers that prevent AAPI professionals from reaching leadership roles despite their overrepresentation in technical fields. 50%+ of Silicon Valley employees are Asian, but only about 20% make it to executive roles.Dave pointed out that some companies, like NVIDIA and Pinterest, have higher promotion parity for AAPI employees, largely due to Asian founders setting the culture from the start.We also discussed how South Asian Americans, particularly men, have navigated leadership differently:“There are cultural differences. South Asian men tend to be more emboldened, better at self-promotion, and more comfortable with confrontation.”“A lot of East Asians were raised with Confucian values—respecting elders, keeping our heads down, not rocking the boat. But in corporate America, if you don’t advocate for yourself, no one else will.”For many East Asian Americans, self-promotion feels unnatural, but as Dave pointed out, you have to play the game:“If you don’t take credit for your work, someone else will.”The Future of AAPI Leadership & The Next GenerationWe also talked about how the next generation of AAPI professionals—kids growing up fully assimilated in the U.S.—might have an easier time navigating leadership.“Our parents had the immigrant scarcity mindset. They came here with nothing, and they made sure we knew it. But our kids? They don’t feel guilt about anything.”While that might sound like a joke, there’s a real shift happening. Younger generations are:* More willing to take risks (vs. prioritizing stability)* More confident in their leadership abilities* Less burdened by cultural expectations to follow "safe" career pathsDave also emphasized the importance of soft skills—something many Asian Americans don’t focus on enough:“Being a good storyteller is one of the most valuable skills you can have. It’s critical for sales, leadership, and career growth.”Navigating AI, Tech Trends, and OpportunitiesAs a product guy, I had to ask Dave about AI trends and where he sees opportunities.“AI is obviously taking over everything, but right now there’s a lot of noise. The biggest opportunities will be in how AI is actually delivered—UX and experience design will be the differentiator.”He also mentioned that consumer tech isn’t getting much VC love right now, but that doesn’t mean there aren’t opportunities:“Everyone is focused on AI, but I think there are some gems out there in consumer tech that people are overlooking.”Final Thoughts: Getting Involved & Building the FutureAs we wrapped up, we touched on a big topic: the growing anti-China sentiment in the U.S. and how it affects Asian Americans.“It’s already happening. Asian hate crimes, the ‘China Initiative’ going after innocent professors—it’s all connected. The best thing we can do is get more involved: tell our stories, support AAPI representation in politics, and build powerful communities.”Dave’s story is a lesson in action. He didn’t wait for permission to start Hyphen Capital. He built the network he wished he had.For anyone reading this—whether you’re a founder, a PM, or an aspiring leader—don’t wait for permission.Build your own house. Advocate for yourself. And always lift others as you climb.Connect with Dave Lu & Fireside PMIf you enjoyed this convo, follow Dave on LinkedIn (@DaveLu) or check out his Substack at DaveLu.com. If you're an AAPI founder looking for funding, visit Hyphen Capital.For more deep dives like this, subscribe to Fireside PM on Substack! And if you're a pre-seed founder looking for advice (or maybe a small check), hit me up. Let’s build something great together. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit firesidepm.substack.com

Hey team,Last week on Fireside PM, I had the pleasure of reconnecting with an old Google colleague, Chris Vander May. We first crossed paths at the Kirkland office nearly 18 years ago, and since then, Chris has had an incredible career spanning Amazon, Google, Meta, and now his own AI-driven startup, Product Partner AI.Chris has seen it all—from launching the first version of Google Meet to leading product and engineering teams at Amazon and Meta. Our conversation covered a range of topics, including the key differences between Google, Amazon, and Meta, the power of controllable inputs in product management, and how AI is reshaping the role of PMs. We also delved into how PMs can future-proof their careers, the evolving nature of AI in product management, and the best ways for PMs to leverage AI-driven insights.Google vs. Amazon vs. Meta: The Cultural DifferencesChris offered a fascinating perspective on how these three tech giants operate differently:* Google: “Google, in my time, was very much an engineering-driven culture. PMs were peers to engineers, and leadership pushed a 'Why not?' mentality—do the harder thing, even if it's more work.”* Meta: “At Facebook, the PM was more of an ideas person. The ability to run rapid experiments at scale changed the dynamic. The role of the PM wasn’t necessarily about making the best decision upfront, but rather about trying things and seeing what sticks.”* Amazon: “Amazon is much more product-led. There’s a strong culture of writing and documentation. You don’t just make decisions on the fly—you write things down to think them through rigorously.”Chris expanded on these insights by discussing how leadership styles differ at these companies. At Google, the focus was often on engineering-driven innovation, requiring PMs to align closely with engineering teams. At Meta, the emphasis was on creativity and rapid iteration, where launching and learning from experiments was key. Meanwhile, at Amazon, data-driven decision-making and operational efficiency were deeply ingrained in the company culture.The Power of Controllable InputsOne of the most impactful topics we covered was controllable inputs, a concept deeply ingrained in Amazon’s culture. Chris explained:“A controllable input is a metric that you directly influence and that drives business outcomes. Unlike vanity metrics that might look good on a slide deck, these metrics are actionable.”He gave a fantastic example from Frito-Lay:“They figured out that the key metric for stocking chips wasn’t total sales, but rather the number of stale bags on the shelf. If there were too many stale bags, they were overstocking. If there were none, they were losing sales. The right number was one stale bag per restock cycle. That’s a controllable input.”For PMs, this means moving beyond simple engagement or revenue numbers to find the metric that actually drives sustainable growth.Chris further elaborated that companies often struggle to identify the right controllable inputs because they conflate outcomes with inputs. A revenue target, for instance, is an outcome, but what actually drives it? Identifying and focusing on those drivers—whether it’s reducing onboarding friction, improving time-to-value, or optimizing conversion rates—is what separates strong PMs from the rest.He emphasized that a good controllable input should have the following characteristics:* Directly Influenced by the Team: PMs and their teams should be able to take action that moves the metric.* Closely Tied to Business Outcomes: While it may not be a direct revenue number, it should be something that, when improved, positively impacts the business.* Quickly Measurable: Metrics that update in real time or within a few weeks allow for faster iteration and learning.* Resistant to Gaming: Vanity metrics like total app downloads can be manipulated through paid acquisition, but a well-defined input resists such distortions.Chris also stressed the importance of refining controllable inputs over time. Many teams initially choose the wrong input and need to course-correct. A well-calibrated controllable input should help guide strategic decisions and enable PMs to set clear goals, allocate resources efficiently, and align teams around measurable outcomes.Another example Chris shared was Amazon’s revenue per thousand opportunities (RPMO) metric for ads:“Instead of just looking at total ad revenue, we focused on how much revenue was generated per thousand ad impressions. This allowed us to optimize for better targeting and engagement rather than simply increasing ad load, which could degrade user experience.”He pointed out that these kinds of inputs serve as a north star for product teams, helping them focus on continuous improvements that compound over time.How AI is Changing the PM RoleAI is transforming the nature of product management, and Chris believes it will lead to fewer but more highly leveraged PMs.“Right now, we have around 900,000 PMs globally. In the future, I think we’ll see fewer PMs, but they’ll be much more strategic, working across more engineers and leveraging AI to do a lot of the traditional work PMs used to do manually.”For aspiring PMs, Chris had some direct advice:* Develop strong judgment. AI can generate ideas, but it can’t (yet) make high-level strategic decisions.* Talk to customers. AI can process feedback, but it still can’t replace the intuition of a PM who truly understands user pain points.* Master AI tools. “If you’re not using AI to make yourself 10x more effective, you’ll be replaced by someone who is.”Chris also discussed the potential impact of AI on cross-functional collaboration. AI-driven insights can help teams make data-informed decisions more efficiently, but it also means PMs will need to refine their ability to translate AI-generated recommendations into actionable product strategies.What’s Next?Chris is now leading Product Partner AI, an AI-powered PM tool designed to help PMs be more effective. If you’re curious, you can check it out at Product Partner AI.For those of you looking to level up your own PM skills, I’ve got a few things going on:* Maven Cohort: A three-month small-group coaching program where we break down your career like a product, define your OKRs, and build a roadmap to success. Apply at Maven.* Coaching: If you have a big product decision, job offer, or career move coming up, I offer direct coaching at TomLeungCoaching.com. I also work with company execs to support the coaching and learning of their younger PM teams.Thanks for tuning in to this week’s Fireside PM! Let me know in the comments—what are your thoughts on controllable inputs in product management?Until next time,Tom This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit firesidepm.substack.com