Fireside Product Management

I met Albino Sanchez in the bleachers at a high school JV football game. While our sons battled it out on the field for Palo Alto High School, we found ourselves deep in conversation about something far removed from touchdowns and tackles: why some product leaders thrive while others crash and burn in seemingly similar companies.Albino doesn’t fit the typical Silicon Valley mold. Born and raised in Mexico City, he spent his early career as a strategy consultant helping large companies implement frameworks like Balanced Scorecard and OKRs. But unlike most consultants who move on to the next engagement, Albino couldn’t stop thinking about his former clients. Some organizations flourished with these frameworks. Others abandoned them within months. The strategic tools were identical. The execution was completely different.What he discovered would fundamentally change how I think about my own career moves—and it should change how you think about yours too.The Pattern That Changes EverythingAfter years of looking back at his consulting clients, Albino noticed something remarkable: “Those organizations that were really thriving with these frameworks and really growing, they had a special type of leader. And that leader was usually a people-centered leader, a leader that was humble, that was a servant leader, and that this leader cared about their people, listened to them, and really wanted collaboration.”This wasn’t just about nice leadership. It was about creating what he calls “the atmosphere for people to thrive.”The insight hit him hard enough that he completely pivoted his career. He became an executive coach, spending the last 15 years working with leaders to shape healthier, more productive cultures. He moved his family from Mexico City to Palo Alto four years ago and recently founded Aha! Impact, a company focused on helping organizations achieve the right culture so both the business and employees can thrive.But here’s what matters for you as a PM: Albino’s journey revealed something most of us learn the hard way. Culture doesn’t just influence whether a strategy succeeds. Culture IS the strategy.Why “Culture Eats Strategy for Breakfast” Isn’t Just a Poster on the WallYou’ve probably seen this quote attributed to Peter Drucker plastered on every startup’s office wall. But do you actually believe it?Albino puts it this way: “We need to have the right environment so people can thrive and then implement and then be successful in business.” Without that environment, even the most brilliant product strategy becomes a document that sits in a Google Drive folder, gathering digital dust.The Culture Paradox: Why Google, Amazon, Meta, and Microsoft All Win DifferentlyDuring our conversation, I pushed Albino on something that had been bothering me. If culture is so critical, how do companies with wildly different cultures all succeed? Amazon’s frugality and bias for action looks nothing like Google’s innovative freedom and psychological safety. Microsoft’s collaborative enterprise focus differs dramatically from Meta’s move-fast-and-break-things mentality.His answer surprised me.While different cultures can succeed, Albino sees clear patterns in what works today: “Innovation is one of them. We need to have nowadays with so many changes with AI, technology, globalization, communications. We need to be innovative. We need to be adaptive. We need to embrace change as something that’s part of our day to day.”The successful organizations aren’t choosing between being people-centered OR innovative OR efficiency-driven. They’re becoming all three simultaneously. The old archetypes (pick your culture and stick with it) no longer apply in our rapidly evolving landscape.But here’s the critical insight for PMs: You need to understand which cultural attributes matter most to you personally. Because while multiple cultures can succeed, not every culture will allow YOU to succeed.The Real Reason You’re Miserable at WorkAlbino shared something that hits close to home for many experienced PM’s: “People join organizations because of the company and they leave the organization most likely because of the boss.”This tracks with every conversation I’ve had as an executive coach. The PMs who come to me aren’t struggling with their OKRs or roadmaps. They’re struggling with leadership dynamics, unclear values, and cultural misalignment.Think about your own career. When you’ve been most energized, most productive, most creative. Was it because of the company mission statement? Or was it because you had a leader who created space for you to do your best work?When you’ve been most miserable, was it really about the compensation or the commute? Or was it about a leader who micromanaged, who didn’t value collaboration, who created an atmosphere of fear rather than trust?Culture doesn’t just make work more pleasant. It fundamentally determines whether you can bring your best self to the job.The Leadership Styles That Shape Product CulturesHere’s where Albino’s work gets really practical. He identifies four primary leadership archetypes that shape organizational culture, and understanding these can help you decode any company you’re considering:1. The Controlling Leader This leader centralizes decision-making, micromanages execution, and views team members as resources rather than collaborators. They might get short-term results, but they create cultures where PMs become order-takers rather than strategic partners. Innovation dies because risk-taking gets punished.2. The Competitive Leader Everything is a zero-sum game. Teams compete internally for resources, recognition, and rewards. This can drive individual performance but often at the expense of collaboration. For PMs, this means product launches succeed but platform thinking fails. You win your battle but lose the war.3. The Collaborative Leader This is Albino’s people-centered leader. They invest in relationships, foster psychological safety, and view success as collective rather than individual. In product organizations, this looks like cross-functional partnerships that actually work, user research that influences decisions, and retrospectives that drive real improvement.4. The Creative Leader These leaders embrace experimentation, tolerate failure, and push for innovation. They create cultures where PMs can propose bold ideas without fear. But without enough structure, these cultures can become chaotic.The best leaders, and the best cultures, combine elements of all four, calibrated to the organization’s specific needs. As a PM evaluating a new role, you need to assess not just the stated values but the actual leadership style you’ll experience day-to-day.The Questions You’re Not Asking in Interviews Most PMs treat interviews as one-way evaluations. The company assesses you; you try to impress them. Albino argues this is backwards.“This is a two-way assessment,” he told me. “You are also interviewing them.”I know what you’re thinking: “Tom, that’s easy to say when you have options. When you’re desperate for a job, you can’t afford to be picky.”I get it. But here’s the truth Albino helped me see: accepting a role at a company with cultural misalignment doesn’t solve your job search problem. It delays your job search problem by six months while making you miserable.Your objective isn’t to get as many offers as possible. Your objective is to get offers from places where you’ll thrive.So what questions should you actually ask?On Work-Life Integration: “How do you manage team collaboration across different locations and time zones?”These aren’t just logistics questions. They reveal whether the company trusts employees or requires surveillance. They show whether leadership believes productivity comes from presence or output.On Decision-Making: “Tell me about a recent product decision where you had significant disagreement among stakeholders. How did you resolve it?”This behavioral question (turned around on the company) reveals their true decision-making process. Do they rely on data, authority, consensus, or customer feedback? Do they value PM input or just expect execution?On Failure and Learning: “Describe a recent product launch that didn’t meet expectations. What happened, and how did the team respond?”The answer tells you everything about psychological safety. Do they blame individuals or examine systems? Do they learn from failures or hide them?On Growth and Development: “How do PMs typically grow in their careers here? Can you share specific examples of PMs who’ve advanced and what enabled their growth?”This reveals whether the culture actually invests in development or just talks about it in the handbook.But here’s Albino’s most important advice: “It’s very important that you are authentic, you are yourself. Don’t try to make an act there. It’s very common to do that just to cover the expectations of the potential employer. But you know what? Try to get rid of that fear and try to be yourself.”This is counterintuitive in a competitive job market. Every instinct tells you to mold yourself to what they want. But cultural misalignment has costs. Stress. Burnout. Short tenure. Another job search in six months.Better to be yourself, assess fit honestly, and find a place where you can actually thrive.How AI Is Changing Culture Assessment Here’s where Albino’s work gets really interesting for those of us in tech. He’s building an AI-powered tool to help companies assess cultural fit during hiring.Traditional culture fit assessment is notoriously unreliable. It often means “do I want to get a beer with this person,” which perpetuates homogeneity and bias. Or it gets delegated to a single interviewer who may not accurately represent the actual culture.Albino’s approach is different. His tool analyzes the organization’s stated values, actual behaviors, and cultural attributes. Then it evaluates candidates against these dimensions through structured assessment.“It’s going to analyze your organizat

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 a

Every few years, the world of product management goes through a phase shift. When I started at Microsoft in the early 2000s, we shipped Office in boxes. Product cycles were long, engineering was expensive, and user research moved at the speed of snail mail. Fast forward a decade and the cloud era reset the speed at which we build, measure, and learn. Then mobile reshaped everything we thought we knew about attention, engagement, and distribution.Now we are standing at the edge of another shift. Not a small shift, but a tectonic one. Artificial intelligence is rewriting the rules of product creation, product discovery, product expectations, and product careers.To help make sense of this moment, I hosted a panel of world class product leaders on the Fireside PM podcast:• Rami Abu-Zahra, Amazon product leader across Kindle, Books, and Prime Video• Todd Beaupre, Product Director at YouTube leading Home and Recommendations• Joe Corkery, CEO and cofounder of Jaide Health • Tom Leung (me), Partner at Palo Alto Foundry• Lauren Nagel, VP Product at Mezmo• David Nydegger, Chief Product Officer at OvivaThese are leaders running massive consumer platforms, high stakes health tech, and fast moving developer tools. The conversation was rich, honest, and filled with specific examples. This post summarizes the discussion, adds my own reflections, and offers a practical guide for early and mid career PMs who want to stay relevant in a world where AI is redefining what great product management looks like.Table of Contents* What AI Cannot Do and Why PM Judgment Still Matters* The New AI Literacy: What PMs Must Know by 2026* Why Building AI Products Speeds Up Some Cycles and Slows Down Others* Whether the PM, Eng, UX Trifecta Still Stands* The Biggest Risks AI Introduces Into Product Development* Actionable Advice for Early and Mid Career PMs* My Takeaways and What Really Matters Going Forward* Closing Thoughts and Coaching Practice1. What AI Cannot Do and Why PM Judgment Still MattersWe opened the panel with a foundational question. As AI becomes more capable every quarter, what is left for humans to do. Where do PMs still add irreplaceable value. It is the question every PM secretly wonders.Todd put it simply: “At the end of the day, you have to make some judgment calls. We are not going to turn that over anytime soon.”This theme came up again and again. AI is phenomenal at synthesizing, drafting, exploring, and narrowing. But it does not have conviction. It does not have lived experience. It does not feel user pain. It does not carry responsibility.Joe from Jaide Health captured it perfectly when he said: “AI cannot feel the pain your users have. It can help meet their goals, but it will not get you that deep understanding.”There is still no replacement for sitting with a frustrated healthcare customer who cannot get their clinical data into your system, or a creator on YouTube who feels the algorithm is punishing their art, or a devops engineer staring at an RCA output that feels 20 percent off.Every PM knows this feeling: the moment when all signals point one way, but your gut tells you the data is incomplete or misleading. This is the craft that AI does not have.Why judgment becomes even more important in an AI worldDavid, who runs product at a regulated health company, said something incredibly important: “Knowing what great looks like becomes more essential, not less. The PM's that thrive in AI are the ones with great product sense.”This is counterintuitive for many. But when the operational work becomes automated, the differentiation shifts toward taste, intuition, sequencing, and prioritization.Lauren asked the million dollar question. “How are we going to train junior PMs if AI is doing the legwork. Who teaches them how to think.”This is a profound point. If AI closes the gap between junior and senior PMs in execution tasks, the difference will emerge almost entirely in judgment. Knowing how to probe user problems. Knowing when a feature is good enough. Knowing which tradeoffs matter. Knowing which flaw is fatal and which is cosmetic.AI is incredible at writing a PRD. AI is terrible at knowing whether the PRD is any good.Which means the future PM becomes more strategic, more intuitive, more customer obsessed, and more willing to make thoughtful bets under uncertainty.2. The New AI Literacy: What PMs Must Know by 2026I asked the panel what AI literacy actually means for PMs. Not the hype. Not the buzzwords. The real work.Instead of giving gimmicky answers, the discussion converged on a clear set of skills that PMs must master.Skill 1: Understanding context engineeringDavid laid this out clearly: “Knowing what LMS are good at and what they are not good at, and knowing how to give them the right context, has become a foundational PM skill.”Most PMs think prompt engineering is about clever phrasing. In reality, the future is about context engineering. Feeding models the right data. Choosing the right constraints. Deciding what to ignore. Curating inputs that shape outputs in reliable ways.Context engineering is to AI product development what Figma was to collaborative design. If you cannot do it, you are not going to be effective.Skill 2: Evals, evals, evalsRami said something that resonated with the entire panel: “Last year was all about prompts. This year is all about evals.”He is right.• How do you build a golden dataset.• How do you evaluate accuracy.• How do you detect drift.• How do you measure hallucination rates.• How do you combine UX evals with model evals.• How do you decide what good looks like.• How do you define safe versus unsafe boundaries.AI evaluation is now a core PM responsibility. Not exclusively. But PMs must understand what engineers are testing for, what failure modes exist, and how to design test sets that reflect the real world.Lauren said her PMs write evals side by side with engineering. That is where the world is going.Skill 3: Knowing when to trust AI output and when to override itTodd noted: “It is one thing to get an answer that sounds good. It is another thing to know if it is actually good.”This is the heart of the role. AI can produce strategic recommendations that look polished, structured, and wise. But the real question is whether they are grounded in reality, aligned with your constraints, and consistent with your product vision.A PM without the ability to tell real insight from confident nonsense will be replaced by someone who can.Skill 4: Understanding the physics of model changesThis one surprised many people, but it was a recurring point.Rami noted: “When you upgrade a model, the outputs can be totally different. The evals start failing. The experience shifts.”PMs must understand:• Models get deprecated• Models drift• Model updates can break well tuned prompts• API pricing has real COGS implications• Latency varies• Context windows vary• Some tasks need agents, some need RAG, some need a small finetuned modelThis is product work now. The PM of 2026 must know these constraints as well as a PM of the cloud era understood database limits or API rate limits.Skill 5: How to construct AI powered prototypes in hours, not weeksIt now takes one afternoon to build something meaningful. Zero code required. Prompt, test, refine. Whether you use Replit, Cursor, Vercel, or sandboxed agents, the speed is shocking.But this makes taste and problem selection even more important. The future PM must be able to quickly validate whether a concept is worth building beyond the demo stage.3. Why Building AI Products Speeds Up Some Cycles and Slows Down OthersThis part of the conversation was fascinating because people expected AI to accelerate everything. The panel had a very different view.Fast: Prototyping and concept validationLauren described how her teams can build working versions of an AI powered Root Cause Analysis feature in days, test it with customers, and get directional feedback immediately.“You can think bigger because the cost of trying things is much lower,” she said.For founders, early PMs, and anyone validating hypotheses, this is liberating. You can test ten ideas in a week. That used to take a quarter.Slow: Productionizing AI featuresThe surprising part is that shipping the V1 of an AI feature is slower than most expect.Joe noted: “You can get prototypes instantly. But turning that into a real product that works reliably is still hard.”Why. Because:• You need evals.• You need monitoring.• You need guardrails.• You need safety reviews.• You need deterministic parts of the workflow.• You need to manage COGS.• You need to design fallbacks.• You need to handle unpredictable inputs.• You need to think about hallucination risk.• You need new UI surfaces for non deterministic outputs.Lauren said bluntly: “Vibe coding is fast. Moving that vibe code to production is still a four month process.”This should be printed on a poster in every AI startup office.Very Slow: Iterating on AI powered featuresAnother counterintuitive point. Many teams ship a great V1 but struggle to improve it significantly afterward.David said their nutrition AI feature launched well but: “We struggled really hard to make it better. Each iteration was easy to try but difficult to improve in a meaningful way.”Why is iteration so difficult.Because model improvements may not translate directly into UX improvements. Users need consistency. Drift creates churn. Small changes in context or prompts can cause large changes in behavior.Teams are learning a hard truth: AI powered features do not behave like typical deterministic product flows. They require new iteration muscles that most orgs do not yet have.4. The PM, Eng, UX Trifecta in the AI EraI asked whether the classic PM, Eng, UX triad is still the right model. The audience was expecting disagreement. The panel was surprisingly aligned.The trifecta is not going anywhereRami put it simply: “We still need experts in all three domains to raise the bar.”Joe added: “AI makes i

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

I didn’t plan to make a video today. I’d just wrapped a client call, remembered that OpenAI had released Atlas, and decided to record a quick unboxing for my Fireside PM community.I’d heard mixed things—some people raving about it, others underwhelmed—but I made a deliberate choice not to read any reviews beforehand. I wanted to go in blind, the way an actual user would.Within 30 minutes, I had my verdict: Atlas earns a C+.It’s ambitious, it’s fast, and it hints at a radical new way to experience the web. But it also stumbles in ways that remind you just how fragile early AI products can be—especially when ambition outpaces usability.This post isn’t a teardown or a fan letter. It’s a field report from someone who’s built and shipped dozens of products, from scrappy startups to billion-user platforms. My goal here is simple: unpack what Atlas gets wrong, acknowledge what it gets right, and pull out lessons every PM and product team can use.The Unboxing ExperienceWhen I first launched Atlas, I got the usual macOS security warning. I’m not docking points for that—this is an MVP, and once it hits the Mac App Store, those prompts will fade into the background.There was an onboarding window outlining the main features, but I barely glanced at it. I was eager to jump in and see the product in action. That’s not a unique flaw—it’s how most real users behave. We skip the instructions and go straight to testing the limits.That’s why the best onboarding happens in motion, not before use. There were some suggested prompts which I ignored but I would’ve loved contextual fly-outs or light tooltips appearing as I explored past the first 30 seconds of my experience:* “Try asking Atlas to summarize this page.”* “Highlight text to discuss it.”* “Atlas can compare this to other sources—want to see how?”Small, progressive cues like these are what turn exploration into mastery.The initial onboarding screen wasn’t wrong—it was just misplaced. It taught before I cared. And that’s a universal PM lesson: meet users where their curiosity is, not where your product tour is.When Atlas StumbledAtlas’s biggest issue isn’t accuracy or latency—it’s identity.It doesn’t yet know what it wants to be. On one hand, it acts like a browser with ChatGPT built in. On the other, it markets itself as an intelligent agent that can browse for you. Right now, it does neither convincingly.When I tried simple commands like “Summarize this page” or “Open the next link and tell me what it says,” the experience broke down. Sometimes it responded correctly; other times, it ignored the context entirely.The deeper issue isn’t technical—it’s architectural. Atlas hasn’t yet resolved the question of who’s driving. Is the user steering and Atlas assisting, or is Atlas steering and the user supervising?That uncertainty creates friction. It’s like co-piloting with someone who keeps grabbing the wheel mid-turn.Then there’s the missing piece that could make Atlas truly special: action loops.The UI makes it feel like Atlas should be able to take action—click, save, organize—but it rarely does. You can ask it to summarize, but you can’t yet say “add this to my notes” or “book this flight.” Those are the natural next steps in the agentic journey, and until they arrive, Atlas feels like a chat interface masquerading as a browser.This isn’t a criticism of the vision—it’s a question of sequencing. The team is building for the agentic future before the product earns the right to claim that mantle. Until it can act, Atlas is mostly a neat wrapper around ChatGPT that doesn’t justify replacing Chrome, Safari, or Edge.Where Atlas ShinesDespite the friction, there were moments where I saw real promise.When Atlas got it right, it was magical. I’d open a 3,000-word article, ask for a summary, and seconds later have a coherent, tone-aware digest. Having that capability integrated directly into the browsing experience—no copy-paste, no tab-switching—is an elegant idea.You can tell the team understands restraint. The UI is clean and minimal, the chat panel is thoughtfully integrated, and the speed is impressive. It feels engineered by people who care about quality.The challenge is that all of this could, in theory, exist as a plugin. The browser leap feels premature. Building a full browser is one of the hardest product decisions a company can make—it’s expensive, high-friction, and carries a huge switching cost for users.The most generous interpretation is that OpenAI went full browser to enable agentic workflows—where Atlas doesn’t just summarize, but acts on behalf of the user. That would justify the architecture. But until that capability arrives, the browser feels like infrastructure waiting for a reason to exist.Atlas today is a scaffolding for the future, not a product for the present.Lessons for Product ManagersEven so, Atlas offers a rich set of takeaways for PMs building ambitious products.1. Don’t Confuse Vision with MVPYou earn the right to ship big ideas by nailing the small ones. Atlas’s long-term vision is compelling, but the MVP doesn’t yet prove why it needed to exist. Start with one unforgettable use case before scaling breadth.2. Earn Every Switch CostChanging browsers is one of the highest-friction user behaviors in software. Unless your product delivers something 10x better, start as an extension, not a replacement.3. Design for Real Behavior, Not Ideal BehaviorMost users skip onboarding. Expect it. Plan for it. Guide them in context instead of relying on their patience.4. Choose a Metaphor and CommitAtlas tries to be both browser and assistant. Pick one. If you’re an assistant, drive. If you’re a browser, stay out of the way. Users shouldn’t have to guess who’s in control.5. Autonomy Without Agency Frustrates UsersIt’s worse for an AI to understand what you want but refuse to act than to not understand at all. Until Atlas can take meaningful action, it’s not an agent—it’s a spectator.6. Sequence Ambition Behind ValueThe product is building for a world that doesn’t exist yet. Ambition is great, but the order of operations matters. Earn adoption today while building for tomorrow.Advice for the Atlas TeamIf I were advising the Atlas PM and design teams directly, I’d focus on five things:* Clarify the core identity. Decide if you’re an AI browser with ChatGPT or a ChatGPT agent that uses a browser. Everything else flows from that choice.* Earn the right to replace Chrome. Give users one undeniably magical use case that justifies the switch—research synthesis, comparison mode, or task execution.* Fix the metaphor collision. Make it obvious who’s in control: human or AI. Even a “manual vs. autopilot” toggle would add clarity.* Build action loops. Move from summarization to completion. The browser of the future won’t just explain—it will execute.* Sequence ambition. Agentic work is the destination, but the current version needs to win users on everyday value first.None of this is out of reach. The bones are good. What’s missing is coherence.Closing ReflectionAtlas is a fascinating case study in what happens when world-class technology meets premature positioning. It’s not bad—it’s unfinished.A C+ isn’t an insult. It’s a reminder that potential and product-market fit are two different things. Atlas is the kind of product that might, in a few releases, feel indispensable. But right now, it’s a prototype wearing the clothes of a platform.For every PM watching this unfold, the lesson is universal: don’t get seduced by your own roadmap. Ambition must be earned, one user journey at a time.That’s how trust is built—and in AI, trust is everything.If you or your team are wrestling with similar challenges—whether it’s clarifying your product vision, sequencing your roadmap, or improving PM leadership—I offer both 1:1 executive and career coaching at tomleungcoaching.com and expert product management consulting and fractional CPO services through my firm, Palo Alto Foundry.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

When I sit down with product leaders who’ve spent decades shaping how Silicon Valley builds products, I’m always struck by how their career arcs echo the very lessons they now teach. Michael Margolis is no exception.Michael started his career as an anthropologist, stumbled into educational software in the late 90s, helped scale Gmail during its formative years, and eventually became one of the first design researchers at Google Ventures (GV). For fifteen years, he sat at the intersection of startups and product discovery, helping founders learn faster, save years of wasted effort, and—sometimes—kill their darlings before they drained all the fuel.In our conversation, Michael didn’t just share war stories. He laid out a concrete, repeatable framework for product teams—whether you’re a PM at a FAANG company or a fresh hire at a Series A startup—on how to cut through noise, get to the truth, and accelerate learning cycles.This post is my attempt to capture those lessons. If you’re an early to mid-career PM in Silicon Valley trying to sharpen your craft, this is for you.From Anthropology to Gmail: The Value of Unorthodox BeginningsMichael’s path to Google wasn’t a linear “go to Stanford CS, join a startup, IPO” narrative. Instead, he started in anthropology and educational software, producing floppy-disk learning titles at The Learning Company and Electronic Arts. That detour turned out to be foundational.“Studying anthropology was my introduction to usability and ethnography,” Michael told me. “It gave me a lens to look at people’s behaviors not just as data points but as cultural patterns.”For PMs, the lesson is clear: don’t discount the odd chapters of your own career. That sales job, that nonprofit internship, or that side hustle in teaching can become your secret weapon later. Michael carried those anthropology muscles into Gmail, where understanding human behavior at scale was just as critical as writing code.Actionable Advice for PMs:* Audit your own “non-linear” career experiences. What hidden skills—interviewing, pattern-recognition, narrative-building—could you bring into product work?* When hiring, don’t filter only for straight-line resumes. The best PMs often bring unexpected perspectives.The Google Years: Scaling Research at Hyper-speedMichael joined Gmail in 2006, when it was still young but maturing fast. He quickly noticed how different the rhythm was compared to the slow, expensive ethnographic studies he had done for consulting clients like Walmart.com.“At Walmart,” he explained, “I had to compress these big, long expensive projects into something faster. Gmail demanded that same speed, but at enormous scale.”At Google, the prime “clients” for his research were often designers. The questions he answered were things like: How do we attract Outlook users? How do we make the interface intuitive enough for mass adoption?This difference matters for PMs: in big companies, research questions often start downstream—how to refine, polish, or optimize. In startups, questions live upstream: What should we build at all? Knowing where you sit in that spectrum changes the kind of research (and product bets) you should prioritize.Jumping to Google Ventures: Bringing UXR Into VCIn 2010, Michael made a bold move: leaving the mothership to become one of the very first design researchers embedded inside a venture capital firm. GV was trying to differentiate itself by not just writing checks but also offering operational help—design, hiring, PR.“I got lucky,” he recalled. “GV had already hired Braden Kowitz as their design partner, and Braden said, ‘I need a researcher.’ That was my break.”Working with founders was a shock. They didn’t act like Google PMs. “It was like they were playing by a different set of rules. They’d say, ‘Here’s where we’re going. You can help me, or get out of my way.’”That forced Michael to reinvent how he showed value. Instead of writing reports that might sit unread, he had to deliver insights in real-time, in ways founders couldn’t ignore.The Watch Party Method: Stop Writing ReportsHere’s where the gold nuggets come in. Michael realized traditional reports weren’t cutting it. Instead, he invented what he calls “watch parties.”“I don’t do the research study unless the whole team watches,” he said. “I compress it into a day—five interviews with bullseye customers, the whole team in a virtual backroom. By the end, they’ve seen it all, they’re debriefing themselves, and alignment happens automatically. I haven’t written a report in years.”Think about that. No 30-page decks. No long hand-offs. Just visceral, shared observation.Actionable Advice for PMs:* Next time you run a user test, insist that at least your core team attends live. Skip the sanitized recap slides.* At the end of a session, have the team summarize their top three takeaways. When they say it, it sticks.Bullseye Customers: Getting Uncomfortably SpecificOne of Michael’s most powerful contributions is the bullseye customer exercise.“A bullseye customer,” he explained, “is the very specific subset of your target market who is most likely to adopt your product first. The key is to define not just inclusion criteria but also exclusion criteria.”Founders (and PMs) often resist narrowing. They want to believe their TAM is huge. But Michael’s method forces rigor. He described grilling teams until they admit things like: Actually, if this person doesn’t work from home, they probably won’t care. Or if they’ve never paid for a premium tool, they won’t convert.Example: Imagine you’re building a new coffee subscription. Your bullseye might be: Remote tech workers in San Francisco, ages 25-35, who already spend $50+ per month on specialty coffee, and who like experimenting with new roasters. If your product doesn’t delight them, it won’t magically resonate with “all coffee drinkers.”Actionable Advice for PMs:* Write down both inclusion and exclusion criteria for your bullseye.* Add triggers: life events that make adoption more likely (e.g., new job, new diagnosis, move to a new city).* Recruit five people who fit it exactly. If they’re lukewarm, rethink your product.Why Five Interviews Is EnoughMichael swears by the number five.“After three interviews, you’re not sure if it’s a pattern,” he said. “By five, you hit data saturation. Everyone sees the signal. Any more and the team is begging you to stop so they can make changes.”For PMs under pressure, this is liberating. You don’t need 100 customer calls. You need five of the right customers, observed by the right team members, in a compressed timeframe.Multiple Prototypes: Don’t Ask Customers to ImagineAnother Margolis rule: never show just one prototype.“If you show one, the team gets too attached, and the customer can only react. With three, I can say: compare and contrast. What do you love? What do you hate? I collect the Lego pieces and assemble the next iteration.”Sometimes those prototypes aren’t even original mockups—they’re competitor landing pages. As Michael joked: “Have you tested your competitor’s prototypes? No? Then you’ve left something out.”Actionable Advice for PMs:* When exploring value props, mock up three different landing pages. Don’t ask “Which do you prefer?” Instead ask: “Which elements matter most, and why?”* Treat mild praise as a “no.” Only visceral excitement counts as signal.Founders, Stubbornness, and the Henry Ford TrapI pressed Michael on what happens when founders dismiss customer feedback by invoking Henry Ford’s famous line about “faster horses.”He smiled. “The beauty of bullseye customers is it forces accountability. If you told me these people are your dream users, and they shrug, then you can’t hand-wave it away. Either change your customer definition or your product.”This is a crucial lesson for PMs who work with visionary leaders. Conviction is necessary, but unchecked conviction can sink a product. Anchoring on bullseye customers creates a shared contract that keeps both egos and hypotheses grounded.Bright Spots > Exit InterviewsWhen teams ask him to interview churned customers, Michael often refuses.“There are a bazillion reasons people don’t use something,” he said. “It’s inefficient. Instead, I go find the bright spots—the power users who love it. I want to know why they’re on fire, and then go find more people like them.”This “bright spot” focus helps PMs avoid premature pivots. Instead of chasing every no, double down on the yeses until you understand the common thread.Case Study: Refrigerated Medications and ZiplineTo illustrate, Michael shared a project with Zipline, the drone-delivery company. They wanted to deliver specialty medications. The core question: was speed or timing more important?Through interviews, the bright spot insight emerged: refrigeration was the killer constraint. Patients didn’t care about “fastest possible” delivery in the abstract. They cared about not leaving refrigerated drugs on their porch.That nuance completely changed the product and infrastructure design.For PMs, the takeaway is that sometimes the decisive factor isn’t the flashy benefit you advertise (“we’re the fastest!”) but a practical detail you only uncover through careful listening.AI and the Future of ResearchWe couldn’t avoid the AI question. Has it changed his process?“I worry about how AI is creating distance between teams and customers,” Michael admitted. “If my bot talks to your bot and spits out a report, you miss the nuance. The power of research is in the stories, the details, the visceral reactions.”That said, he does use AI for quick prototype copywriting and summaries. But he insists on live team observation for the real work.For PMs, the advice is to use AI as an accelerant, not a replacement. Let it write the rough draft of your landing page copy, but don’t outsource customer empathy to a transcript.What PMs Should Do Differently TomorrowLet’s distill Michael’s 15 years of wisdom into actionable steps you can implement this week:* Define your

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 thinkin

When Jess Gilmartin talks, I listen. If you've been in Silicon Valley long enough, you might have heard of Jess. She's been a full-time CMO, a founder, a startup whisperer, and most recently, one of the sharpest advisors to CEOs I know when it comes to hiring marketing leadership that actually works.In our recent Fireside PM conversation, we went deep on the do's and don'ts of hiring a CMO. While many of my listeners and readers are early- to mid-career product managers, this interview is packed with insight relevant not just to founders and CEOs but to any PM who will eventually be part of a hiring panel, collaborating with marketing peers, or considering their own path to executive leadership.Why Your Company Even Needs a CMOLet’s start with first principles. As Jess puts it:“The CMO is the steward of the brand. And brand isn’t just your website or ads—it’s every interaction a customer has with your company. That includes your support team, your social media presence, your onboarding experience, and yes, your product.”The reason this matters for PMs is simple: we often underestimate the scope and gravity of the brand experience. We build features. We define roadmaps. But we rarely think of the emotional resonance of what we’re building.“Part of the job is ensuring consistency and excellence across all these touchpoints,” Jess said. "That also means having the spine to flag when something the product team is doing will degrade that experience."Translation? If you think marketing's job is to "wrap" your product after the work is done, you're missing the point.What Great CMOs Actually Do (Hint: It’s Not Just Marketing)One of the biggest wake-up calls for me was hearing Jess talk about the real job of a modern CMO:“When I was a CMO, I had senior leaders under me running product marketing, growth, and comms. I spent most of my time on executive alignment, crisis communications, and internal messaging. I was rarely in the weeds.”That division of labor is a signal. The difference between a head of marketing and a CMO isn’t just title inflation—it’s scope. A CMO thinks in systems. They think in multi-stakeholder alignment. And above all, they should be one of the CEO’s most strategic advisors.Jess broke it down this way:“The biggest mistake founders make is hiring too senior or too junior a marketer for where they are. If you're still pre-product-market-fit, don’t hire a head of marketing. You need to be doing that work yourself.”As someone who has worked with a lot of pre-PMF startups, I couldn’t agree more. And yet, time and time again, I see companies try to paper over early churn or stagnant growth with splashy campaigns and SEO spend.It doesn’t work.Product Managers: Here’s What You Keep Getting WrongThere was one part of our conversation where my PM blood pressure rose just a bit. I asked Jess what she does when she’s in a cross-functional meeting and the product team is proudly showcasing something... that isn’t actually great for the user experience.She smiled:“I try not to have strong opinions on product. That’s not my job. But I deeply understand the customer experience. And when I see something that isn’t going to land, I raise a fuss. Not all the time—you have to pick your battles—but marketing sees across silos. We’re often the ones that spot inconsistencies in the end-to-end experience.”PMs, listen carefully to that last part.We often live in silos—focused on our vertical, our feature, our sprint velocity. Meanwhile, marketing is scanning horizontally, sensing what happens when someone tries to connect the dots. That perspective is invaluable. And if you're lucky enough to work with a CMO or a senior PMM who raises their hand about UX inconsistencies or cross-functional misalignments, treat that as signal, not noise.The Dirty Truth About CMO TenureReady for the most sobering stat of the interview?“Most CMOs last two years,” Jess said flatly.Why? Expectations are sky-high. CEOs want the creativity of Nike, the analytics of Facebook, the virality of TikTok, and the demand gen of HubSpot—all in one human. Oh, and don’t forget crisis PR, event strategy, and internal morale-boosting Slack posts.That level of sprawl is untenable.“Marketing is the only function where we expect a single person to be excellent at creative, numbers, product thinking, storytelling, operations, hiring, and analytics,” she said. “It’s unrealistic.”So what happens? You hire a CMO for one phase, they nail it, and then two years later the business needs something else. That’s not a failure. That’s reality.Founders and PM leaders should take note: you’re not hiring a CMO to last forever. You’re hiring them to solve today's problem exceptionally well.Demand Gen vs. Messaging vs. PMM: Pick Your PoisonThis next insight is gold for any hiring manager:“When hiring a marketing leader, figure out what your biggest problem is. Is it lack of pipeline, weak differentiation, or lack of strategic product alignment? You won’t find someone world-class at all three.”Jess described three typical archetypes:* Demand Gen-focused leaders – Performance-oriented, data-driven, often strong in growth loops and paid acquisition but weaker on storytelling or product narrative.* Brand and Messaging experts – They come up through storytelling, design, and content. These are the campaign artists and identity shapers.* PMM-style CMOs – Strong in positioning, go-to-market, launch orchestration, and cross-functional strategy. They see the product and customer journey clearly but may lack deep growth or brand skills.That might be the most important hiring advice in this entire conversation. Every CMO candidate comes from somewhere. What they did before will influence what they do next. The key is aligning that background with your immediate business challenge.If you already have a rockstar PMM but no repeatable pipeline, hire a demand gen-oriented CMO. If you’ve got leads but they don’t convert or your brand is invisible, find a storytelling operator.And if you're a PM moving up the ranks? This is how you should evaluate your marketing counterparts. Don’t just ask "are they good?" Ask: are they good at the thing we need most right now?Hiring CMOs: Skip the Case Study, Do the PlanWhen Jess advises founders on hiring a CMO, she doesn’t run them through generic behavioral interviews or vague culture fit chats. She makes them present a real plan.“I give them a budget. I give them our current strategy. I ask: 'Show me how you’d spend it and what your plan would be to hit our goals.'”The best candidates, she said, are:* Articulate – They speak clearly, persuasively, and inspire confidence.* Specific – They don’t just say "we’ll run paid ads" or "we’ll increase brand awareness." They tell you how, why, and in what sequence.* Bold – They bring creative energy. One candidate impressed Jess with cheeky, bold challenger messaging that she herself wouldn’t have dreamed up.That kind of spark matters. Especially for a role that’s supposed to shape how the world feels about your company.Founders: Don’t Get Dazzled by LogosPerhaps the spiciest take in the conversation came when I asked Jess about resume signals:“Do not get dazzled by former companies. That senior PMM from Salesforce may not have ever hired a team, built a pipeline, or touched brand messaging.”This hit close to home. As a former Google exec, I know all too well how much people over-index on logos. Jess prefers candidates who have been in the trenches—startup veterans, operators who’ve hired across functions, people with range.The ultimate test? Jess asks: Did they just run the playbook, or do they know how to build one?Actionable Advice for PMsSo, what should early- and mid-career product managers take from this?* Learn to speak marketing. Understand the difference between PMM, brand, growth, and demand gen. This makes you a better cross-functional partner.* Invite your PMM early. Don’t treat them as a launch afterthought. Bring them into ideation, prioritization, and roadmap planning.* Observe how marketing fights. Good CMOs don’t just object; they escalate. They build coalitions. Watch how they influence.* Test CMO fit with real-world scenarios. Ask candidates to brainstorm a real strategic decision or messaging conflict. See how they think.* Beware the shiny logo. Ask CMO candidates what they personally owned, who they hired, and what they changed. If you hear too much passive voice, dig deeper.A Final WordIf you're a founder or exec looking to hire your first CMO, I strongly suggest you watch the full interview. And if you're a PM, use this as a lens to reflect on your own career. How well do you understand your marketing counterparts? How would you describe your company's brand? Learn more about Jess here.If you'd like help with your own product leadership journey, I offer 1:1 coaching at tomleungcoaching.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

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 subscrib

Twenty-five years ago, Tim DeSieno and I were two outsiders on the tropical island of Singapore, me trying to build a startup, him fresh out of a restructuring law practice. We reconnected recently on the Fireside PM podcast, and what followed was one of the most illuminating conversations I've had this year.Tim's career arc is anything but conventional: from decades in global debt restructuring to litigation finance investor, and now advisor to an AI legal startup. The conversation, which started as a reunion, turned into a firehose of insight—for lawyers, founders, and especially product managers trying to anticipate where disruption lands next.This post distills that hour-long conversation into key lessons for early- and mid-career product managers. Whether you're wrangling roadmaps at a Series A startup or driving platform strategy at a late-stage unicorn, you'll find practical frameworks, surprising analogies, and a peek into the wild intersection of law and AI.1. Litigation Funding Is What Early VC Investing Looks Like in a Non-Tech Industry"We would look at 100 cases, take three seriously, and maybe fund one."Tim described litigation finance as a "venture capital" approach to legal claims. Funders underwrite the legal equivalent of startups: high-risk, high-reward lawsuits with uncertain outcomes. The investment model is classic VC—non-recourse funding in exchange for a percentage of winnings—but applied to torts, sovereign disputes, and commercial litigation.This is a also a class in triage. As PMs, we're sometimes guilty of over-indexing on tech, TAM or user demand without enough scrutiny of distribution or defensibility. In litigation finance, everything must be strong: the legal basis, the plaintiff’s character, the likelihood of enforcement.Actionable Advice:* When evaluating new bets, use a PM version of Tim’s triangle: Strength of case, rational actor, enforceability. Substitute your product’s domain as needed. If your bet falls apart on any leg, kill it early.* Don’t be afraid to walk away. "We’d spend weeks researching only to discover a fatal flaw." Avoid sunk cost fallacy.2. The Real AI Gold Rush Isn’t Just Generation, It’s PredictionHarvey (the legal AI startup backed by OpenAI) gets the headlines, but Tim is on the board of an earlier stage adjacent player called Canotera. Instead of drafting, Canotera predicts litigation outcomes. Think of it as a risk analytics layer built from all New York legal precedents, offering lawyers (and insurers, GCs, even arbitrators) a probabilistic view of their odds."It’s like calling up a senior partner and getting a second opinion—except this one has read every case."This isn't just a better way to write memos. It's a decision-making accelerator.Product Insight: There are many types of AI value in any vertical:* Efficiency (do more, faster)* Accuracy (better outcomes)* Confidence (de-risking decisions)Harvey is largely #1 and #2. Canotera is going hard at #3.Actionable Advice:* When building AI products, map your feature set to these value levers. Which one are you really selling?* Don’t sleep on #3—especially in regulated or high-stakes domains, confidence trumps speed.3. Adoption Gaps Aren’t Just Technical—They’re Psychological"The number of people in law who haven’t touched ChatGPT is shockingly large."Sound familiar? We’ve all worked with that PM, eng lead, or exec who in late 2022 who thought gen-AI was a toy. The parallel to law is stark: many lawyers fear AI not because it's ineffective, but because it threatens their identity.In both professions, billing hours and writing decks have long been proxies for value. When those tasks are automated, the insecurity is real.Actionable Advice:* Frame AI as augmentation, not replacement. Tim noted the firms that are thriving are those that say, “Yes, we bill per hour—but we’ll use AI to deliver more per hour.”* Early adopters are not just tech-savvy—they're secure enough to rethink their role. When evangelizing AI, target the curious and the confident.4. “Doctrinal vs. Practical” Isn’t Just a Law School Problem"You come out of law school, and you're good at arguing both sides. But no client wants that."Tim called out how legal education—especially the Socratic case method—trains great thinkers but poor practitioners. Law grads often need years of on-the-job experience before they become useful to clients.Sound like any junior PMs you know?Product teams are often full of doctrinal thinkers—people great at debating frameworks, prioritization models, or vision decks. But if you can’t turn that into a working prototype, a roadmap aligned with GTM, or a tough tradeoff call, you’re not adding value.Actionable Advice:* “Thinking like a PM” (strategy, ambiguity, storytelling) is necessary but not sufficient. Pair it with executional reps early in your career.* If you’re a manager, give your ICs reps they can own end to end. Treat it like an apprenticeship, not just a theoretical seminar.5. Liberal Arts Still Matter—Even in the Age of AGI"If you can’t write it clearly, you don’t own it."Tim made a powerful case for the liberal arts as the antidote to AI passivity. He sees students turning in polished work generated by LLMs but lacking any real grasp of the content. Writing, he argues, is thinking. If you can't articulate a point unassisted, your judgment muscles don’t get built.Actionable Advice:* Don’t outsource the first 70% of a product brief, strategy doc, or roadmap to ChatGPT. Use AI to refine and stress-test, not originate.* Push yourself to learn something uncomfortable. Tim’s litmus test: "Do hard things that are new to you. That’s how you grow judgment."6. You’re Not Competing With AI, You’re Competing With Humans Using It Better"A junior lawyer with AI tools can be more valuable than a senior one without."In a decade, your job won't be taken by AI—but it might be taken by someone with 5 years less experience who knows how to pair human empathy with AI speed.Actionable Advice:* Learn prompt engineering, yes—but also get great at evaluating AI output. That judgment layer is what companies will pay for.* Practice defending ideas live, without a script. At some point, someone will ask, “Why did you make that decision?” Be ready.7. Forecasting the Endgame: When Courts Run on Code"Maybe one day litigation disappears—two parties upload their facts, the machine decides, and that’s enforceable."While Tim was cautious to say this vision is far off, the implications are worth pondering. What happens when not just lawyers, but judges, juries, and arbitrators are augmented—or replaced—by machines?Whether or not this comes to pass, the lesson is clear: no profession is immune. If law can be automated, so can most knowledge work. And product managers will either ride that wave—or be washed away.In ClosingAs PMs, we love talking about disruption—but we rarely get to see it play out in an industry as slow-moving and tradition-bound as law. That’s what made this conversation with Tim DeSieno so instructive. Law is changing. AI is changing. And the humans who thrive are the ones who stay curious, adaptable, and relentlessly focused on value—not ego.If this resonated, I offer 1:1 coaching for product leaders at tomleungcoaching.com, and PM consulting through 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

“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

If you had asked me five years ago whether product managers would need to worry about AI-generated job applicants or remote interviews with operatives from North Korea, I would've laughed you out of the room. But here we are.I recently sat down with Shannon Anderson, longtime recruiter and talent scout at Madrona Ventures, for an episode of the Fireside PM podcast. What started as a reaction to a viral LinkedIn post ended up being one of the most wide-ranging, eye-opening conversations I've had on the topic of recruiting, AI, and the future of work. In this post, I want to unpack that conversation for my fellow PMs—especially early to mid-career professionals—because it’s not just a hiring manager problem. The game has changed, and you need to play it differently.1. The AI Arms Race Has Arrived—in RecruitingOne of Shannon’s first points hit hard:“Everything we learned about remote hiring during COVID was a sea change, but it’s already obsolete.”AI isn’t just being used to help candidates polish their resumes. It’s being used to impersonate them entirely. We’re seeing fake LinkedIn profiles, AI-altered Zoom video filters, and entire teams coordinating to pass coding screens. In some extreme cases, Shannon shared concerns (echoed by Cisco and others) about foreign actors infiltrating companies via fake hires—not for the paycheck, but for access to corporate IP.And if you think you’re safe because you’re hiring PMs, not engineers, think again. AI-generated product portfolios, hallucinated case studies, and polished-but-shallow cover letters are already flooding inboxes. As a PM, you need to be aware that your competition isn’t just smart—they may be synthetic.2. Fundamentals Still WinEven though tools like ChatGPT can make anyone look great on paper, Shannon makes the case that they can’t replace taste or judgment.“You can throw something into ChatGPT and so can I. But if you haven't developed judgment, you won't know if it's good. That's the difference.”This is a wake-up call for early-career PMs. AI can help you draft a PRD or write your resume, but if you can't tell when something feels off, you're at a disadvantage. So don’t just use AI to do your job—use it to learn how to do your job better. Treat it like an intern, not your brain.3. Referrals Matter More Than EverOne of the simplest but most actionable takeaways from Shannon was this:“Referrals reign supreme. Warm intros from trusted networks slice through AI noise like butter.”That line stuck with me. Because in a world of keyword-stuffed resumes and AI-generated portfolios, what cuts through is trust. If you’re a PM looking for your next gig, your best bet isn’t just optimizing your resume—it’s cultivating your network. Build authentic relationships with people you admire. Offer to help. Ask for advice. That’s how you earn the referrals that will put you on the shortlist.4. Speed and Specificity MatterWhen it comes to hiring, Shannon noted that the best candidates are snapped up quickly, especially in sales and customer-facing roles. This has lessons for product managers too:* Be decisive: If you're a PM hiring a researcher, analyst, or designer, you can't drag your feet.* Be precise: Know what you actually need in the next 30, 60, or 90 days. Shannon emphasized:“If you don’t know what you’re solving, you’ll never know who to hire.”For PMs trying to break into the role, this also means tailoring your pitch. Don’t be the generalist applying to every PM role. Be the best fit for a specific company’s specific need—and show you understand their business.5. Beware the Amazonification of HiringShannon made a provocative analogy:“Hiring managers want an Amazon shopping experience. Search, shortlist, get reviews, place the order, and return the bad ones.”But people aren't bunion pads. As PMs, we have to resist this mindset—whether we’re hiring or being hired. Great hiring takes time. It takes context. It takes iteration. The more we treat talent like widgets, the more we hurt our teams.So what can you do about it?Actionable Advice for PMs in 20251. Always be recruiting. Shannon's mantra for hiring managers applies just as well to candidates. Talk to people. Stay curious. Keep your resume sharp even when you’re not looking.2. Build judgment the hard way. Do the work. Write PRDs by hand before prompting AI. Read other PMs' docs. Critique them. Get feedback. Learn what good looks like.3. Use AI—but don’t outsource your thinking. AI is great at suggestions. You’re responsible for decisions. Treat it as a brainstorming partner, not a crutch.4. Referrals > Resumes. Spend 10x more time building relationships than updating bullets. Help others first. Ask for warm intros. It works.5. Embrace customer-facing roles. If you can't land a PM job, a sales engineer, support, or success role can give you skills in empathy, communication, and product insight. Note from Shannon: If you’re at a company with a sales team looking for top-notch sales interns from UW’s Foster School’s Professional Sales Program, request an intern here.In the end, Shannon reminded us of the most important principle:“You are who you hire.”Check out Shannon’s substack here.As PMs, we build our careers and our products the same way: one thoughtful decision at a time. Let’s make good ones.If you're looking for 1:1 coaching, visit tomleungcoaching.com. For product consulting and strategy support, visit 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

I rarely read career books cover to cover. But when I listened to Search in Plain Sight by Somer Hackley, I was hooked. It wasn’t a blog post padded into a book—this was the real deal. Structured, thorough, and full of insights that I wish I had twenty years ago.Naturally, I invited Somer onto the Fireside PM podcast to dig deeper. What followed was a masterclass on how executive search firms actually work, what most job seekers get wrong, and how product managers (PMs) can be far more effective in today’s hiring market.This post distills our conversation and offers practical takeaways for early to mid-career PMs in Silicon Valley.1. First, Understand the Recruiter’s Job Is Not to Get You a JobMost people think executive recruiters are job-finders. They are not."Recruiters are filling positions for the companies that have hired them," Somer explains. "Job seekers think recruiters help them get jobs—that’s the No. 1 misconception."Retained search firms, like Somer’s, work for companies. Their job is to find the top 5 people in the world for a specific role. They aren’t career counselors. If you email them out of the blue asking, "Can you shop me around?" you’re starting off on the wrong foot.Instead, try this:* Introduce yourself briefly.* Acknowledge they may not have a role for you now.* Ask to stay in touch and offer something useful (referrals, trends, etc.).Which leads us to…2. Givers Get Remembered"The best way to lodge yourself in someone’s brain is when they want to talk to you."If a recruiter reaches out to you about a role, take the call. Even if it’s not a fit, this is your shot to build a real relationship. Give them referrals. Share industry insights. Offer to help.Even cold outreach can work—if it’s memorable and has value. But better than cold outreach is being referred by someone they trust."Awesome by association is a real thing."Want to stand out in a recruiter’s memory months or years later? Make yourself memorable and refer great people. That moves you into the "awesome bucket."3. Shift From Chronology to ClarityMost PMs walk recruiters through their resumes chronologically. Don’t.Instead, use the "Think of me when…" framework."I view recruiting as journey matching, not just title or industry matching."What Somer means is this: articulate the kind of journey you help companies with.For example:* "Think of me when you’re taking a Series B startup through its first platform rebuild."* "Think of me when you need a PM to lead a 0 to 1 AI product with regulated data."Be specific. Counterintuitively, the more focused your ask, the more opportunities you’ll attract."If you say you do everything, you're not memorable. If you're specific, people will actually ask if you can do other things too."Nail your "Think of me when…" and you’ll win more attention and more fit.4. Build Momentum Before You’re Job HuntingIf you wait until you’re actively looking to reach out to recruiters, it’s already too late."Check in once a quarter when you're not looking. Keep doing that. Then 10 years go by and you're top of mind."Also, don’t expect a quick match. Recruiters have to work with the roles their clients hire them to fill. That may or may not line up with your profile today. So think long term.5. Your Mindset Is Your FoundationThe hiring process is brutally uncertain. You can be perfect for a role and still not get it."It's not always about the perfect fit. Sometimes it's timing, politics, or just who else is in the mix."So how do you stay sane?Somer recommends two things:* Surround yourself with a personal board of champions."People who want you to win."* Use curiosity to stay grounded."If you approach things with curiosity, you’ll take the edge off the pressure to impress."I’d add: don’t take silence personally. If a recruiter ghosts you for two weeks, it probably has nothing to do with you.6. Be Transparent with Trusted RecruitersShould you tell a recruiter if you’re not sure about a role? Or if you're juggling other offers?Somer says yes—if the recruiter seems trustworthy and aligned."If I put you forward, I want you to win. So tell me what's really going on. Then I can help position things properly with the client."Good recruiters aren’t trying to lowball you. They’re trying to avoid surprises that make everyone look bad."Let us be your buffer."The caveat: if you get a bad vibe, trust it. Not every recruiter is great. But when you find one who is, work with them, and be honest.7. In Final Rounds, It’s About RiskIf you make it to the last stage of interviews, here’s the real secret:"They’re looking for the safe choice. Not the flashiest."That means:* Be likable.* Be prepared.* Show you’ve done this kind of thing before.* Ask smart questions that show you understand what success will really take.As a former hiring manager at Google, I can confirm: often, multiple candidates are great. The final choice often comes down to small things: a strong reference, cultural fit, or someone who just de-risked themselves better.8. Own the Compensation ConversationSomer's advice here was nuanced and spot-on.* Talk about comp early and often.* Don’t wait until the offer.* Create clarity about your expectations and what you’d be walking away from."It’s a multi-channel conversation. We're talking comp on the first call, the second, the fourth. I want to make sure the offer you get will be accepted."Use tools like:* What offers you’re currently seeing* What equity you’re leaving behind* Comp benchmarks from recruiters or friendsAnd always be clear on your own ask:"You don’t have to give a single number. Just be prepared with ranges that reflect your walkaway points."9. Don’t Be Afraid to Ask Hard QuestionsCandidates often avoid asking tough questions late in the process. They worry about seeming ungrateful or negative.But:"Asking good questions gives the client comfort that you're thinking about this the right way."Ask about challenges, gaps, political realities. Ask what’s not working. If this role might be yours, you need to know. And you earn respect by showing you want to succeed, not just land the job.10. After You’re Hired: Stay in TouchRecruiters who place you can become allies for life. Keep the relationship warm."Once you can text someone, it's easy to stay in touch."You never know when you’ll:* Build a team and need help hiring* Be looking again* Have someone to referA short "Thinking of you" or "Saw this post and thought of you" goes a long way.Final ThoughtsThis conversation was packed. If you haven’t already, pick up Somer’s book Search in Plain Sight or listen on Audible. You’ll walk away smarter, more grounded, and better prepared to navigate your next search.I’ll leave you with this:“Most people show up saying 'Here’s my bio.' The best ones show up saying 'Think of me when…'”Let that guide how you introduce yourself from now on.If you’re navigating your next big career move or want guidance on positioning yourself more strategically, I offer 1:1 coaching at tomleungcoaching.com.If you’re building out a product org and need help hiring or structuring the team, visit paloaltofoundry.com for consulting options. 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

Earlier on the Fireside PM podcast, I sat down with Carl Wu, a veteran product leader who built and launched an AI-first product from scratch—targeting one of the most conservative and risk-averse professions out there: immigration law.Carl's story isn't just a case study in GenAI product development. It's a case study in how technical intuition, product fundamentals, and real-world empathy for users come together when you're building for high-stakes use cases. If you're building (or planning to build) AI-native products—especially in a vertical domain—this one's for you.From Code to Customer: Carl's Unusual ArcCarl started his career as an engineer at Microsoft before transitioning into product. He later built search engines at Tencent and led teams building video and ML-powered systems at startups.His technical fluency isn't just a badge of honor; it's the lens through which he approaches product thinking. "The biggest mental switch," he said, "was thinking less about system optimization and more about user optimization. But having that technical foundation helped me build credibility and intuition."That background came in handy when he joined a legal-tech startup as their founding AI PM, tasked with turning foundational models into real customer value.AI is Powerful. PM Fundamentals Still Matter More.Carl didn’t come in trying to train the biggest model or chase the buzziest trends. His first question was simple: What’s the most painful, expensive problem we can solve with this tech?That led to a set of vertical AI theses:* Focus on domains where language is the product* Prioritize workflows with high structure and high stakes* Use the LLM for synthesis, drafting, and structured transformationLegal fit perfectly. Immigration law, in particular, had everything he wanted: repeatable document types, expensive expert time, and huge amounts of unstructured data ripe for automation.Carl explained:"We were working in immigration law, and saw that some law firms were outsourcing their drafting to journalists because the petitions were so complex. That was the lightbulb. If someone is paying a human writer to stitch together legal arguments, an LLM might be able to help."That insight narrowed the use case to a single visa type—one that law firms actively avoided because of the overhead.Actionable Advice: Find the Burning ProblemToo many PMs start with the model and go hunting for a problem. Carl did the reverse:* Pick a high-value domain* Talk to users (lawyers)* Observe workflows* Identify pain so acute that firms were outsourcing or avoiding itTakeaway for PMs: Your GenAI MVP shouldn't be an experiment. It should be a wedge into a critical workflow where users already know they need help.Taking the Technology Risk So the User Doesn’t Have ToCarl had a tough call to make: Should they require users to fill out guided prompts and forms, or should they lean fully into autonomous generation from source docs?He chose the latter, betting that removing all user friction—even at the cost of increased technical risk—would pay off."I decided that in a 0-to-1 product, especially one this disruptive, we should optimize for user experience and absorb complexity on the system side."The result? Documents that used to take lawyers six months to draft could now be generated and reviewed in 48 hours.Prompt Engineering Is a System, Not a SkillOne of the most eye-opening parts of our conversation was how Carl talked about prompt systems. Not as static prompts. Not as clever tokens. But as a full-stack orchestration layer that included:* Smart retrieval from unstructured documents* Chained prompts and intermediate reasoning steps* Evaluation systems to assess output quality"It’s not just writing a good prompt," Carl said. "You need a full evaluation stack. In our case, that included using GPT-4.5 as an evaluator model to score drafts generated by cheaper, faster models."For example:* Drafts were scored on legal logic, writing style, and argument rigor* Outputs were linked back to citations and source documents to reduce hallucinations* Users could rate and comment on individual sections to create a feedback loopPro tip for PMs: Build your evaluation stack early. Hallucinations are product-killers in high-trust domains. Don’t rely on vibes.Integration and Compliance Are Features, Not AfterthoughtsOne of the hardest parts of going from demo to deployment was integration with legacy systems—and gaining trust from clients concerned about privacy and compliance."Clients are asking new questions now. Who trained your model? Where is the data stored? How do we know our documents aren’t being used to retrain the model?"This is where Carl's vertical AI strategy paid off. By focusing on a niche domain, the team could:* Build tight integrations with specific case management tools* Offer clear guarantees around data residency and model usage* Design workflows that mirrored existing processes, not replaced themWhat Carl Would Do DifferentlyDespite the success, Carl reflected on one thing he might have underinvested in:"In hindsight, I think we could’ve done more on the user experience layer. Not just the data outputs, but how those outputs are presented, edited, and refined by the user. UX is perception. And perception is reality."He pointed to Midjourney as an example:* Many models can generate images* But Midjourney added affordances like zoom, re-prompt, and edit* That made the tool feel alive, adaptable, and human-friendlyTakeaway: Don’t treat UI as a wrapper. It’s a co-pilot.What PMs Get Wrong About AIWe wrapped up the conversation with one of my favorite questions: What do most PMs get wrong about AI?"PMs overestimate what AI can do and underestimate the importance of the core use case. Just because it feels magical doesn’t mean you can skip the fundamentals."In other words:* Don’t get blinded by novelty* Solve a real, valuable problem* Make it work before you make it scaleFinal Thoughts: It’s a Golden Age for Scrappy BuildersCarl ended our conversation with a quiet bombshell:"Five years ago, people would assume you'd need a 30-person team to build this. Today, a handful of builders can launch vertical AI startups serving million-dollar use cases."That stuck with me.We’re not just witnessing the rise of foundational models. We’re seeing the birth of a new generation of product teams—tiny, focused, fast-moving, and capable of punching way above their weight.If you're early in your PM journey, and you want to be part of this shift:* Learn the fundamentals (value, user pain, workflows)* Embrace ambiguity (AI is still unpredictable)* Be technical enough to evaluate what's feasible* Be empathetic enough to know what mattersAnd if you want help accelerating your journey, I offer 1:1 coaching at tomleungcoaching.com and product consulting services 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

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

I recently had a fascinating conversation with Rodolfo, a Senior Product Manager at Spotify and a good friend from my days mentoring at Harvard Business School. Rodolfo shared a powerful insight into nailing product management interviews, particularly valuable for anyone early to mid-career in Silicon Valley or looking to break into tech. His experience underscores something I frequently coach my own clients about: how you think is more important than what you think.When Rodolfo shared a LinkedIn post and Substack article detailing an interview hack he'd discovered, I knew it was too good to keep to ourselves. Here, I’ll break down the core idea, share key quotes from Rodolfo, and offer actionable advice on applying his insights in your career journey.Rodolfo’s Journey to Product ManagementRodolfo’s path into product management wasn't a straight line. He started in consulting and quickly realized it wasn't his passion. His first real taste of tech came through an operations role at Facebook, which eventually opened his eyes to product management:“Working closely with PMs at Facebook opened my eyes. I started taking on PM tasks and growing into the role by shadowing and volunteering for extra work—essentially making my own PM apprenticeship.”This proactive approach served him well. He transitioned to a PM role at Reddit, pursued an MBA at Harvard, and later joined Cameo to develop deeper business and product skills. Today, Rodolfo leads a zero-to-one team at Spotify, building new user acquisition products—his “dream job,” given his passion for music.Why Product Management?When I asked Rodolfo why he ultimately chose PM, his reasons were relatable:"As someone who thrives on ambiguity and enjoys navigating people, product management was a perfect match. I love switching contexts throughout the day—engineering, design, business strategy—it’s never repetitive."This diversity is appealing, but he cautions:“Don't jump into PM just because it's the hot thing. You need a hypothesis about why you're doing it, and then actively test it. Intern, volunteer, create something yourself—don’t wait for an official onboarding path.”This mirrors my experience advising aspiring PMs: those who wait for structured training or perfect circumstances often miss out. The role itself demands proactive initiative and the courage to make things happen.The Interviewing Breakthrough: Clarity of ThoughtRodolfo described his early struggles with PM interviews. Despite feeling competent in day-to-day product work, he often stumbled when interviewing because he focused too much on frameworks and getting the "right answer." His breakthrough came during an interview practice with a friend, who bluntly told him:“I’m having trouble following your thought process. Can you explain your steps more clearly?”That simple feedback was Rodolfo’s "aha moment." He realized the key to acing interviews isn't necessarily arriving at the perfect solution immediately but clearly articulating the reasoning behind each step of your process.The PM Interview Hack: Communicate Your ThinkingHere's Rodolfo’s hack for improving PM interview outcomes:1. State your assumptions clearly:* “I’m assuming Disney Parks and Resorts wants me to focus on enhancing physical experiences rather than digital-only products. Does this align with your expectations?”2. Articulate each step of your process explicitly:* “I’ve identified that Disney has underutilized assets after closing hours. This might represent untapped revenue opportunities. Let’s explore that.”3. Check in frequently:* “Does this approach make sense? Are these user segments resonating with you?”3. Self-correct visibly:* If you sense misalignment, pause and say, “I think I might be veering off course. Can you clarify if I’m addressing your question directly?”This practice accomplishes two critical objectives:* Ensures the interviewer understands your logic and communication style.* Demonstrates your adaptability, a vital skill for PMs dealing with ambiguity.Real-World ApplicationRodolfo emphasized this isn’t just an interview technique; it’s foundational to successful PM work:“If someone can't follow your thought process in an interview, they won’t follow it at work. Being clear in your thinking is essential to rallying cross-functional teams, convincing stakeholders, and leading effectively.”Indeed, clear communication can differentiate you significantly, especially as your career progresses into roles requiring greater alignment, influence, and strategic clarity.Interviewing Mindset MattersYour mindset during interviews matters tremendously. If you approach it like a high-stakes test, anxiety and rigidity often sabotage performance. Rodolfo and I agreed that treating interviews more like collaborative working sessions makes candidates more successful. As I frequently advise:“Treat your PM interview as a collaborative workshop, not a final exam. Engage your interviewer as if they're a colleague you're collaborating with to solve interesting problems.”Three Quick Actionable Tips for PM Interview Prep:1. Practice transparent thinking:Simulate interviews by verbalizing your reasoning aloud at every step.2. Ask clarifying questions proactively:This demonstrates confidence and ensures alignment throughout the interview.3. Research your target companies deeply:Demonstrating specific knowledge about recent company initiatives or competitors shows genuine interest and sets you apart.Quotes to Remember:* “Nailing an interview is more about the how than the what.”* “If someone can course correct during an interview, it almost makes them a better hire than someone who had the right answer from the start.”* “You get promoted as a PM not just because of results but because of how you achieve those results.”Final ThoughtsRodolfo’s insights highlight the foundational importance of clear thinking and communication, essential skills for anyone aspiring to grow in product management. Embracing these practices can dramatically shift your interviewing—and career—trajectory. Subscribe to Rodolfo’s substack here.Keep Growing Your Product CareerIf you found this conversation helpful and want to dive deeper, I offer personalized 1:1 coaching specifically tailored for product management professionals. You can find out more and book sessions at tomleungcoaching.com.Additionally, if your organization needs support with product strategy, hiring talented PMs, or PM onboarding and training, visit paloaltofoundry.com to learn about my product management consulting services.OK, now 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