Fireside Product Management

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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

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 pro

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. Decidi

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

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 sm

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, s

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 ex

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

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-

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 v

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 Matte

“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, no

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

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 cham

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 themselv

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

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 DifferentlyDespi

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 re

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 wit