The Way of Product with Caden Damiano

Caden Damiano
  • 3.8 (6)
  • TECHNOLOGY

The Way of Product is a philosophy magazine disguised as a podcast. Every week I publish two conversations with people who build in technology and product. Each one comes with a narrative essay that puts you inside the conversation through my eyes — what surprised me, what I kept thinking about after we hung up, where the principle actually lives once you strip away the jargon. I don't hand you the answer. I put you in the room and let you find it yourself. www.wayofproduct.com

  1. 10H AGO

    # 170 Jake Stauch, Co-Founder of Serval: Bet before the technology works, build infrastructure over raw models, and scale enterprise AI reliability

    Jake Stauch is the Co-Founder and CEO of Serval, an AI-native platform that automates enterprise employee support through natural language-to-code workflow generation. Rising to prominence in the mid-2010s as a founder and product executive at the intersection of hardware and enterprise software, Stauch became known for identifying friction bottlenecks in IT automation and building infrastructure-first AI systems before the underlying technology fully matured. Serval, co-founded in April 2024 alongside CTO Alex McLeod, reached a billion-dollar valuation within 18 months of founding after raising $125 million across three rounds led by General Catalyst, Redpoint Ventures ($47M Series A), and Sequoia ($75M Series B). Previously, as Director of Product at Verkada from 2019 to 2024, Stauch spent five years conducting customer discovery with enterprise IT departments across physical security hardware and software. There, he identified the automation paradox that would become Serval’s founding insight: despite a growing landscape of automation tools, most IT requests were still handled manually because the friction of building workflows exceeded the cost of doing the tasks by hand. His product work at Verkada spanned new product lines in physical security cameras, access control systems, and alarm hardware sold to Fortune 500 IT departments. Earlier, Stauch founded NeuroPlus, a brain-sensing hardware and cognitive performance software company, which he led as CEO from 2012 to 2019. He was recognized on the Forbes 30 Under 30 list in 2017 for this work, which included a patent for an EEG-based neurofeedback system. He holds a degree from Duke University. Listen to this episode on Spotify or Apple Podcasts The infrastructure-first philosophy that helped Serval raise $125M at a billion-dollar valuation within 18 months of founding. Jake Stauch left a comfortable Director of Product role at Verkada to start an AI company before AI coding was reliable. “To be very clear,” he tells me, “the early version of our product did not work.” I ask him about this and he does not flinch. He and his co-founder Alex McLeod quit their jobs and started building Serval when the best you could get from an AI coding tool was Copilot autocomplete. The vision was a system where you describe a workflow in natural language and the AI writes the code to make it happen. In the spring of 2024, that vision was aspirational at best. The models hallucinated. The outputs were unreliable. You could get something functional if you force-fed the system enough examples and kept the scope narrow, but production-grade it was not. “It was so close to working,” Jake says, “that we had a lot of confidence.” I have been listening to a lot of Founders Podcast lately, and one pattern David Senra keeps surfacing is the missionary founder -- someone who bets on themselves before the evidence justifies it. Steve Jobs spent $50 million bankrolling Pixar for 10 years with no business model, no market, nothing. Pure belief that the right team would figure it out. I share this with Jake and he grins. The difference with Serval is that Jake was not betting blind. He had spent five years at Verkada watching enterprise IT departments struggle with the same problem: powerful automation tools that nobody used because building the automations took longer than doing the work by hand. He knew the market pain was real. The bet was on the technology catching up. What fascinated me was how Jake thought about that bet. He draws a comparison to wireless communication, and it is the kind of analogy that changes how I think about infrastructure. “Wireless communication is not very reliable at the physical level,” he says. “There’s a lot of loss of signal, loss of data, challenges and interference and all these problems. But we’ve built such robust systems that account for all of that and can mitigate all of that, that we have the experience that wireless is very reliable.” He pauses. “I felt the same was true in the early days of AI. Even if it doesn’t get all that much better, I bet that we’re not even tapping into all the things we could do to build infrastructure on top of this to really take advantage of it.” This reframes the entire AI startup calculus. Most founders I talk to are betting on the models getting better. Jake bet on building engineering systems that make existing models reliable enough to ship. The models improving was a bonus, not a requirement. I bring up Spotify. Gustav Soderstrom, their Co-CEO and head of Product, talked about how Spotify’s early differentiator was not the streaming technology itself but a creative engineering trick: play the first 30 seconds of a song instantly from a smaller file, then use that buffer time to load the rest in the background. The macro trend of better internet connections would eventually make this unnecessary, but they did not wait for the trend. They built infrastructure to deliver the experience now. Jake nods. “I think we’re reaching a certain frontier in a lot of ways, at least in the basic consumer interaction,” he says. “We haven’t even scratched the surface though on what you could do with the fundamental technology.” He extends the wireless analogy further. The gap between basic radio communication and everything we do today with Bluetooth and WiFi is enormous -- and the fundamental physics have not changed. The innovation was all infrastructure. The hardest part, Jake tells me, was deciding when to build for the models as they were versus when to wait for the next generation to make your work obsolete. In the early days -- summer and fall of 2024 -- every model update completely changed their assumptions. They leaned toward betting on improvement. Over time, that shifted. Now Serval builds to make existing models perform at the highest possible level, regardless of what comes next. “You had to make all these decisions with imperfect information,” he says. “We generally leaned towards assuming that the models were gonna make most things better.” There is a timing discipline here that most AI founders miss. The window between too early and too late is narrow, and it keeps moving. Jake caught it because he was not just watching the technology improve -- he was watching enterprise IT departments drown in the same problems year after year. The demand side was stable. The supply side was accelerating. Eighteen months later, Serval has raised $125 million, reached a billion-dollar valuation, and is penetrating markets dominated by legacy players like ServiceNow. Not because the models got better -- though they did -- but because Jake and his team built the infrastructure layer that made unreliable technology reliable enough to ship. “Man, over the past 20 years,” Jake says near the end of our conversation, “what all these software platforms could do outpaced anyone’s ability to actually implement and use them.” He says this almost casually, but it lands like a thesis statement for the entire AI infrastructure era. The models are powerful. The implementations are not. The companies that win will be the ones building the bridge. Jake Stauch started building that bridge before the other side was visible. That is what missionary founders do. Subscribe to the wayofproduct.com for more in depth guest profiles that are worth the time to read. Get full access to The Way of Product w/ Caden Damiano at www.wayofproduct.com/subscribe

    45 min

  2. 3D AGO

    #169 Radhika Dutt, Author of Radical Product Thinking & 5x Acquisition Veteran: Build puzzle-setting cultures, escape OKR perverse incentives, and enable psychological safety

    Radhika Dutt is the author of Radical Product Thinking, a product leadership movement and book that has been translated into multiple languages, including Chinese and Japanese. Rising to prominence in the 2010s and 2020s, she became known for codifying a vision-driven alternative to iteration-led product development used by teams across industries from fintech to government. She currently serves as Advisor on Product Thinking to the Monetary Authority of Singapore (MAS), where she helps steer digital transformation and user-centric product delivery at one of Asia’s most influential financial regulators. Previously, as Author and Speaker at Radical Product Thinking starting in 2017, Dutt built a global practice around a five-part methodology spanning vision, strategy, prioritization, execution and measurement, and culture. Her work equips organizations to diagnose and cure “product diseases” such as feature bloat and metric-driven drift, enabling leaders to align teams around a clear, shared change they seek to bring about in the world. Through keynotes at conferences like Productized and client work with startups and large enterprises, she has trained thousands of product practitioners and executives on how to translate vision into a repeatable operating system for innovation. Her career highlights include founding two companies that were successfully acquired, contributing to a total of five acquisitions across broadcast, media and entertainment, telecom, advertising technology, and robotics over more than 20 years in product. As an MIT-trained engineer with an S.B. and M.Eng. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology (1995–2000), she has applied product thinking to domains as varied as consumer apps, government services, and even wine, demonstrating the portability of her framework across sectors measured in billions of dollars of market value. She is widely regarded as an influential figure in the product management community for shifting organizations away from purely metric- and OKR-driven roadmaps toward what she calls “vision-driven transformation.” Listen to this episode on Spotify or Apple Podcasts Discover the root cause analysis methods and narrative-driven measurement that prevent feature factories while maintaining innovation velocity. “It’s b******t statements, right, that are slim on the details.” Radhika Dutt doesn’t hedge when describing most product visions. Twenty-five years after founding her first startup at MIT with a vision to “revolutionize wireless,” she can admit what most product leaders won’t: she has no idea what that meant. The company had five co-founders, dorm room origins, and all the trappings of a Silicon Valley success story. What it didn’t have was clarity about the problem they were solving. “I don’t even know what we meant by that,” she says, and something shifts in her tone. The polished product consultant gives way to someone examining an old wound. “But it was this idea of just being big, scaling. Now, you know, even today when you look at so many Silicon Valley startups, that’s sort of the mistake you often see, right?” She calls these mistakes product diseases. Not problems or challenges—diseases. The language is deliberate. Diseases are things you catch without realizing it, things that spread through organizations, things that require diagnosis and systematic treatment rather than quick fixes. The disease at that first startup was hero syndrome: the obsession with scale and growth without understanding what problem needs solving. But Radhika discovered something worse during her subsequent career across five acquisitions. Most product teams suffer from multiple diseases simultaneously, creating what she now recognizes as an epidemic of confused priorities and wasted effort. “And I call them product diseases because it’s just so ubiquitous and we need to talk openly about these product diseases. ‘Cause you know, it’s just so easy to catch.” The solution she developed—radical product thinking—starts with a fill-in-the-blanks approach to vision setting that forces teams to confront what they’re actually trying to accomplish. Not the aspirational version, not the pitch deck version, but the detailed, actionable version that can guide daily decisions. “So today, when amateur wine drinkers want to find wines that they’re likely to like and to learn about wine along the way, they have to find attractive looking wine labels or find wines that are on sale. This is unacceptable because it leads to so many disappointments and it’s really hard to learn about wine in this way. We are bringing about a world where finding wines you like is as easy as finding movies you like on Netflix. We are bringing about this world through a recommendations algorithm that matches wines to your taste and an operational setup that delivers these wines to your door.” She pauses after reciting this vision for her wine startup, which she founded in 2011 and sold in 2014. “Now this is a radical vision because I hadn’t told you anything about my startup, and yet hopefully when I shared this vision, you knew exactly what we were doing and why we were doing it.” The contrast with “revolutionize wireless” is stark. One vision contains a specific customer segment, their current painful experience, why that experience is unacceptable, the desired future state, and the concrete mechanism for achieving it. The other contains marketing language that could apply to any telecommunications company. But even teams that develop clear visions struggle with what Radhika calls the second product disease: hyperemia. The obsession with moving numbers up and to the right, regardless of whether those numbers drive long-term value. “You know, the moment I say this, people are usually like, oh yeah, I get it. We have it. Hyperemia is this obsession with moving numbers up and to the right. Having all sorts of wonderful dashboards that all look green. But those are not even necessarily the right metrics. And sometimes they may even be the right metrics, but they drive you in the wrong direction.” The dating app industry provides her favorite example of hyperemia in action. When Tinder launched swipe left/swipe right in 2013, user engagement metrics exploded. Every other dating app copied the mechanic because the numbers looked incredible. User engagement up, time on app up, all the key performance indicators trending toward success. “So, you know, everyone was thrilled with these metrics, but what was happening if you looked at the longer term effect? The more they gamified intimacy, it was creating a toxic dating environment, the more it was dehumanizing interactions. And so what it created in the long term was user fatigue.” The result: dating app backlash, mass user deletions, and in 2025, Bumble laying off 30% of its staff. The entire industry fell into a slump because short-term metric optimization destroyed the long-term value proposition. The numbers looked great right up until they didn’t. “So my point is, hyperemia is one of these diseases where you can do fantastic and making numbers look great. And genuinely they may be the right numbers, but that’s not necessarily good for your product or good for your business in the long term.” This is where most conversations about metrics and OKRs devolve into tactical debates about choosing better numbers or preventing gaming. Radhika thinks those discussions miss the fundamental issue: goals and targets create perverse incentives regardless of how carefully they’re designed. “Even when someone doesn’t have malicious intent and they’re not trying to game metrics, the subconscious incentive you have is to show you’re a high performer and therefore focus on the numbers that look good, that show OKRs to be green, as opposed to focus on numbers that, you know, OKRs aren’t even measuring, but that are indicating a problem and that say, hey, there’s something off here.” She illustrates with her experience at Avid, the company behind video editing software used for every Oscar-winning film in Hollywood. The numbers looked fantastic—sales targets consistently hit or exceeded. But underneath the green dashboards, a different story was unfolding. “If you just looked under the hood, you would see a different scenario. The way we were hitting our sales targets was by moving further and further into the high end because our low end was being eroded by Apple and Adobe.” The company was achieving its goals by retreating upmarket as competitors commoditized the lower tiers. The sales numbers stayed strong, but the strategic position was deteriorating. Instead of asking why the low end was being eroded or how Apple and Adobe’s business models differed, leadership focused on maintaining the metrics that made them look successful. “The incentive is I wanna show that I’ve hit those goals and targets things are working. I wanna prove that our, that things are going well.” This dynamic—prioritizing the appearance of success over understanding reality—is what legendary Intel CEO Andy Grove meant when he said leaders are the last to know. When you set goals and targets, everyone wants to tell you the good news. Bad news gets buried because it threatens the narrative of progress. The alternative Radhika proposes isn’t better goal-setting. It’s puzzle-setting. Instead of declaring what numbers teams should hit, leaders should define what problems need solving and create frameworks for teams to investigate those problems systematically. “So what I am working on in this next book. And what I advocate for is a mindset shift instead of goals and targets. It’s a mindset of puzzle setting and puzzle solving. And then the way you measure people is how well are they solving this puzzle? Are we making progress towards so

    50 min

  3. APR 2

    #168 Saurabh Sharma—Delegate IC work to AI agents, restructure hiring criteria, and build compounding advantage

    Saurabh Sharma is the Chief Product Officer at You.com, where he leads product, design, and research for AI agents that power critical business workflows across search and enterprise use cases. Rising to prominence in the 2010s through work at Google, he became known for scaling applied AI, search and discovery, and trust and safety systems to hundreds of millions of users globally. He is widely regarded as an influential figure at the intersection of AI assistants, consumer products, and infrastructure for large-scale machine learning. Previously, as Head of Search Products at OpenSea, Saurabh led a multi-product portfolio spanning search, discovery, trust and safety, and core web and mobile platforms during the 2022–2023 NFT market cycle. He became known for steering product strategy in a period when OpenSea supported millions of users and billions of dollars in NFT trading volume annually, focusing on safe discovery and high-intent search in a volatile, web3-native marketplace. His leadership aligned search quality, fraud prevention, and creator-centric experiences in an ecosystem that operated 24/7 across global markets. His career highlights include an 11-year tenure as a Group Product Manager at Google, where he led teams of more than 12 product managers and 100 engineers building AI-powered experiences in Google Assistant, Search, Maps integrations, identity, and monetization from 2011 to 2022. At Google, he helped ship and scale products such as Google Assistant’s AI search integrations, Family Link and Google Accounts for kids, Google+, and Gmail, each serving hundreds of millions of monthly active users and operating across more than 100 countries. Earlier, as an Advisory Software Engineer at IBM from 2005 to 2010, he developed core AIX UNIX kernel infrastructure for virtual memory, including Active Memory Expansion and Large Segment Aliasing, contributing to enterprise systems that powered thousands of high-availability servers worldwide. He pairs this low-level systems background with an applied AI product lens shaped by dual BS and MS degrees in Electrical and Computer Engineering from Carnegie Mellon University. In addition to his operating roles, Saurabh has invested in and supported early-stage voice and AI startups through Google Assistant’s strategic investment programs, including seed and Series A bets in companies such as Instreamatic, Voiceflow, and Slang Labs. As a member of the Skip Community, he collaborates with a network of current and former heads of product who collectively bring hundreds of years of leadership experience across AI, fintech, cybersecurity, e-commerce, and renewable energy, shaping best practices for how modern product organizations are structured and scaled. Listen to this episode on Spotify or Apple Podcasts Learn how a CPO at a billion-dollar AI company is rethinking what “good” looks like for PMs — prioritizing strategic thinking over feature-building as software commoditizes. “You gotta be laser sharp about where you can really add value versus what’s being rapidly commoditized.” Saurabh Sharma, CPO at You.com, doesn’t deliver this line as career advice. It’s operational reality. When AI can generate user research insights in minutes and prototype features faster than most teams can write specifications, the entire foundation of product management value shifts. The skills that made someone a great PM five years ago might make them unemployable five years from now. “Where is there a compounding advantage? Where is there a value creation that will be hard to commoditize?” he continues, and I can see him working through the implications for his own hiring decisions. “And that’s a lot of what I think about at the company. That’s a lot of what I try to help my team think about as well.” This isn’t abstract strategy. It’s survival math. You.com processes over a billion web search API queries per month for companies including DuckDuckGo, Windsurf, and Harvey. They raised $100 million at a $1.5 billion valuation. At that scale, every hiring decision carries weight. Every capability they build internally has to justify itself against what they could buy or automate. The question Saurabh faces daily: when AI can handle most IC work, what human skills become more valuable rather than less? “I think what’s really changed is you gotta be laser sharp about where you can really add value versus what’s being rapidly commoditized,” he explains. “And so I think what we’ve seen at You.com is that there’s a continuous focus on where is the value really being created versus where will the value be rapidly commoditized.” The math is brutal but clarifying. If anyone can build a basic SaaS product with AI assistance, then building basic SaaS products isn’t a differentiating capability. If anyone can synthesize user research or analyze competitor data with AI tools, then those skills command lower wages and less organizational influence. But here’s what Saurabh has observed: some capabilities become more valuable as their supporting infrastructure gets commoditized. Strategic judgment becomes more important when you can test more strategies. Pattern recognition becomes more critical when you have more data to parse. The ability to choose which problems are worth solving becomes essential when solving problems gets easier. “And I think it does change how you hire, in that you want people that are able to think that strategic line more so than, well, here’s this cool feature I wanna build.” The hiring implications ripple through every product organization. The PM who excels at writing detailed PRDs and coordinating feature launches might struggle in an environment where PRD writing is automated and feature quality is determined by rapid iteration rather than upfront specification. But the PM who can identify which customer problems create sustainable advantage, who can spot market opportunities before competitors, who can build conviction around directions that don’t yet have validation—those skills compound as the tactical work gets easier. “Well, the cool feature—the customer might be able to replicate it themselves in a way that’s even more fit for them,” Saurabh continues. “It’s more about where is there a compounding advantage? Where is there a value creation that will be hard to commoditize?” I push him on this. How do you interview for strategic thinking? How do you distinguish between someone who talks strategically and someone who thinks strategically? Most product candidates can articulate frameworks and principles. Fewer can demonstrate judgment under uncertainty. “I think that taking that more strategic approach, what separates a middle manager from an executive,” he responds, drawing a connection I didn’t expect. “Nobody told me that I should spend more time with the sales team. But what I noted was, first of all, sales likes having product on road trips with them. It helps customer conversations. But the other part of it was it helps me. It helps me build my worldview. What my roadmap should be.” The example crystallizes the difference. Strategic thinking isn’t about having better frameworks or more elegant presentations. It’s about making connections that aren’t obvious, taking actions that aren’t prescribed, developing conviction through firsthand exploration rather than secondhand analysis. When Saurabh decided to spend more time on sales calls, he wasn’t following a playbook. He was following a hunch about where his learning edge was. That hunch—and the willingness to act on it—represents the kind of judgment that becomes more valuable as tactical execution gets automated. But this creates new tensions in how product teams operate. When strategic judgment becomes the scarce resource, how do you structure teams to maximize it? How do you delegate the increasing scope of work that AI can handle without losing touch with the details that inform strategy? “None of us are gonna be ICs anymore,” Saurabh says, quoting You.com CEO Richard Socher. “We are all gonna be managers in the future. Some of us will continue to manage people, but your traditional IC will now be managing a fleet of agents that’s doing a lot of work for them.” The transition from IC to manager isn’t just about career advancement. It’s about cognitive load distribution. When AI can handle research, analysis, and initial synthesis, human intelligence gets freed up for higher-order work: choosing which questions to ask, interpreting ambiguous signals, making bets on uncertain outcomes. But managing AI agents requires different skills than managing humans. Humans can fill in context, interpret vague instructions, escalate when they’re confused. AI agents do exactly what you ask them to do, which means the quality of your instructions determines the quality of their output. “Many of the emails I write, I will pass through AI to help me with tone or help me think about the way I want to get to a particular objective in a given customer situation,” he explains, describing his own evolution. “That is essentially an example of offloading something that we all know how to do. I could write that perfect email to a customer to diffuse a complex situation, but it might take me an hour to really think through it and get it right. What I found is that email is now five minutes away working with AI.” The email example is tactical, but the implications are strategic. When routine communication becomes effortless, you can maintain relationships at scale that were previously impossible. When difficult conversations can be crafted quickly, you can engage in more of them. The scope of what one person can manage expands dramatically. This expansion creates competitive advantage for individuals and organizations that adapt quickly. But it also creates new forms of inequality. People who learn to m

    58 min

  4. MAR 30

    #167 Anya Cheng, Founder & CEO of Taelor: Master Selection Criteria Over Ideas and Ship MVPs That Actually Teach You Something

    Anya Cheng is the Founder and CEO of Taelor, an AI-powered menswear rental and styling platform at the intersection of fashion, data, and artificial intelligence. Rising to prominence in the 2010s after leading product teams at Meta, eBay, Target, and McDonald’s, she became known for scaling digital products that touched hundreds of millions of users while bridging consumer behavior, growth, and personalization. Today she is widely regarded as an influential figure in fashion tech and serves as faculty at Northwestern University, translating operating experience into curriculum on integrated marketing and product strategy. Previously, as a senior product leader at Meta, eBay, Target, and McDonald’s, she owned global initiatives that drove measurable business outcomes across eCommerce, food delivery, and retail. At McDonald’s she helped lead the global rollout of mobile ordering to thousands of stores, transforming how customers interacted with a brand serving more than 60 million people per day. At Taelor, her team has raised approximately $2.3 million in pre-seed funding, achieved over 10 million marketing impressions with zero ad budget, and earned recognition such as Inc.’s 2025 Best in Business – Best Startup category and Webby Award honors. Her career highlights include award‑winning marketing campaigns at Sears and Kmart, scaling cross‑border digital commerce at eBay, and driving omnichannel experiences at Target that combined stores, mobile, and online into a unified customer journey. As founder of Taelor, she has built an AI-driven styling engine that mixes acquired competitor data, human stylists, and feedback loops from thousands of garment rentals to improve recommendations and reduce fashion waste. Along the way she has been named to Girls in Tech’s “40 Under 40,” delivered a TEDx talk on perseverance, and built a following of more than 28,000 professionals who track her work across AI, circular fashion, and consumer technology. As a book author, startup advisor, and frequent podcast guest, Cheng documents the path from Taiwan to Silicon Valley and distills lessons on resilience, go‑to‑market execution, and human‑centered AI. As a teacher at Northwestern University and a sought‑after speaker at industry events like NRF and SF Tech Week, she helps the next generation of founders and operators understand how to turn data, storytelling, and product intuition into enduring companies. Listen to this episode on Spotify or Apple Podcasts The framework Meta uses in PM interviews to separate great product thinkers from idea generators. “Nobody used the feature besides a product manager,” Anya Cheng tells me. “Why?” She’s describing a project from her time at Target. The team wanted to build store GPS—beacon-powered navigation so customers would never forget an item on their list. They spent six months and millions of dollars mapping every item location in stores with different layouts and footprints. They geo-fenced the shelves. They built the feature. They launched it. “Come on,” she says. “Mom is going to a Target store to get lost. They want to go to a store wandering around and buy stuff.” The Target moms didn’t need efficiency. They needed escape. The Starbucks inside is the feature. The cup holders on the cart are the feature. The permission to wander for an hour away from noisy kids is the feature. The team had solved the wrong problem perfectly. Anya Cheng is the founder and CEO of Taelor, an AI-powered menswear rental subscription. Before founding Taelor she was Head of Product at Meta for Facebook and Instagram Shopping, Head of Product at eBay for Latin America and Africa, led mobile and tablet e-commerce at Target, and was Senior Director at McDonald’s launching their global food delivery apps. She teaches product management at Northwestern and has won 20-plus industry awards. The Target GPS story is one she uses to teach the most important lesson she knows: the quality of your execution is irrelevant if you’re solving the wrong problem. “If you are taking away the value prop,” she says, “then your product is just not going to be popular.” Target’s value proposition isn’t convenience. It’s discovery. It’s the opposite of a GPS. The beacon team understood the technology. They understood the implementation challenge. They just didn’t understand why moms go to Target. I ask Anya how she avoids the same trap. How she decides what to build and—more importantly—what not to build. Her answer is a framework she’s used at Meta, eBay, McDonald’s, and now Taelor. It starts with the Facebook PM interview question: if you’re the product manager of X, what feature would you launch? She’s been on both sides of this question hundreds of times. The candidates who fail are the ones who answer it. “Two types of person,” she says. “One type will be out of the interview loop right away. The other will at least get to the second level.” The first type jumps to solutions. I’d build this, I’d build that. Ideas are cheap. ChatGPT can come up with ideas. That’s not the job. The second type starts with personas. She gives me the birthday product example. Three personas: the birthday person who wants to be surprised, the close friends who want to organize and are afraid of forgetting, and the acquaintances who just want to say happy birthday. Each has distinct pain points. Each pain point sits on a spectrum of severity, frequency, and relevance to Facebook’s unique position. “Then you come up with selecting criteria,” Anya says. “Which pain point is more painful? Which pain point has more people with that pain point? Which pain point is Facebook more relevant to solving versus other people?” The criteria filter the problem space before you ever touch solutions. Then when you do generate solutions, you filter again: which solution solves the problem best, which takes fewer engineering hours, which fits the direction of the business? “Up to here,” she says, “I haven’t told you anything about the solution.” She brings up the same framework when she tells me about Google Shopping versus Facebook Shopping. Same goal: sell things online. Completely different products. Google’s mission is organizing the world’s information, so Google Shopping became price comparison. Meta’s mission is bringing the world closer together, so Facebook Shopping became community commerce—friends selling bicycles from their backyard, influencers sharing product recommendations. “Exactly the same goal,” she says. “But totally different product because it’s different mission of the company.” The mission is the highest-level selection criterion. It determines which problems are yours to solve and which aren’t. The Target beacon team forgot this. They selected a problem—moms forgetting items—that was real but irrelevant to why people went to Target in the first place. Anya’s own origin story follows the framework precisely. At Meta, she was dealing with imposter syndrome—a Taiwanese immigrant surrounded by Ivy League engineers. She needed to look good. She tried Stitch Fix (had to buy everything), Rent the Runway (had to browse 100,000 garments). She realized fashion companies designed for fashion lovers, not for people who wanted to get ready and get on with their day. So she did product 101. Interviewed people. Found that her real persona wasn’t women like her—it was busy men. Sales guys, consultants, pastors, executives. People who didn’t care about fashion but cared deeply about the outcomes fashion enabled: getting a job, closing a deal, landing a date. The MVP was a Shopify landing page with a stock photo of blue shorts. A realtor from San Diego put his email in, waited two months, found Anya on LinkedIn, and called her. They bought clothes from Macy’s during a Christmas sale and shipped from the post office. “Became our first customer,” she says. “The MVP still worked.” It worked because the hypothesis was right. The problem was real. The selection criteria—not the solution—validated the business. Everything that followed—the 150 brand partnerships, the AI-augmented styling, the circular fashion model—was built on the foundation of understanding what the customer actually needed. She tells me about another failed product: eBay’s AI-powered listing tool. Snap a photo of a bicycle, AI writes the description. Built it. Shipped it. Nobody used it. Small sellers on eBay have sentimental attachment to their items. They want to write their own descriptions. Efficiency wasn’t the pain point. Pride was. “If you don’t deeply understand the customer persona, the insider psychology, the job to be done,” she says, “it’s just very hard to build a great product.” I bring up vibe coding—the trend of PMs building functional prototypes with AI tools on weekends. Her intern did exactly this: came back with three working features built in a weekend. Her response was blunt. “This is how exactly at Meta we don’t hire people.” The features might have been good. But they were selected by enthusiasm, not criteria. The intern skipped the framework—the personas, the pain points, the filtering—and went straight to building. AI made it possible to skip the hard work. And skipping the hard work is exactly the failure mode that produces Target store GPS. “In the old time,” Anya says, “you have three ideas and you have to go convince your engineer and designer. And they will challenge your logic. But now you can skip all of this.” The challenge was the quality filter. Removing it doesn’t make you faster. It makes you wrong more efficiently. I ask Anya what she wants product leaders to take away from all of this. She doesn’t hesitate. “We are all problem solvers,” she says. “Go to the meeting. Forget that you are a designer, forget that you are PM, and really focus

    56 min

  5. MAR 26

    #166 Maxine Anderson, Co-founder & CPO at Arist: Iterate Positioning Relentlessly and Ship What the Market Needs

    Maxine Anderson is the Co-founder and Chief Product Officer at Arist, where she helps build what is widely regarded as an emerging default enablement system for large enterprises. Rising to prominence in the early 2020s, she became known for transforming text-message learning experiments into an agentic enablement platform that operates directly inside Slack, Microsoft Teams, and SMS. Under her product leadership, Arist has evolved from simple SMS-based courses to an AI-driven “enablement team in your pocket” that automates needs analysis, content creation, and delivery for distributed workforces at scale. Previously, as Co-founder and Chief Product Officer at Arist, Anderson helped expand the company’s initial seed funding to $3.9 million in 2021 and later raise a $12 million Series A round to fuel rapid enterprise adoption. Her work turned an early Y Combinator-backed idea into a venture serving over 20 Fortune 500 organizations, with pricing starting around $1,000 per month for enterprise deployments. She became known for shipping AI-powered tools such as Creator and the Enablement Agent, which process thousands of complex documents, translate into 100+ languages, and generate ready-to-deliver programs in under eight minutes while proving impact through end-to-end analytics. Her career highlights include co-founding Project W, a student-led organization launched in 2021 to foster interdisciplinary collaboration among women innovators and entrepreneurs across the Babson, Olin, and Wellesley (BOW) colleges, which built an online community of more than 300 members and incubated Project Pods for high-level ventures. As a founding member of College Ventures Network and VP of Marketing at eTower, Babson’s premier entrepreneurial living community whose alumni companies have generated more than $3 billion in combined valuations and over $50 million in funding, she honed a model for building tight-knit entrepreneurial ecosystems. Graduating magna cum laude from Babson College in 2022 with a focus on entrepreneurship, she combined academic honors with hands-on leadership roles that emphasized measurable impact and community scale. Outside of her primary operating role, Anderson serves as a Board Member at Delphian School, bringing startup execution and product thinking back into the education system where she was once Student Council President and a three-time state champion cheerleading captain. Through ongoing advisory work and public writing on enablement, AI agents, and performance diagnostics, she has become an influential figure for operators building the next generation of enterprise learning and HR technology. Listen to this episode on Spotify or Apple Podcasts How Arist navigated seven years of positioning iteration in an undefined category and why shared conviction about the game you’re playing gives product the agency to say no. “We are a new category without ever having created or yet created a category, which is hard to sell,” Maxine Anderson says. There’s no frustration in it. Just the accumulated weight of seven years spent explaining something that doesn’t have a name. Maxine is the co-founder and CPO of Arist, a platform that delivers employee training through Microsoft Teams, SMS, and WhatsApp instead of video-based learning management systems. She started the company at Babson College with two co-founders after they each independently discovered that text-based communication drove behavior change in ways traditional mediums couldn’t. The student in Yemen who could only learn via text. The public speaking coach who sent WhatsApp reminders before talks. Maxine’s own financial literacy programs on Native American reservations where classroom formats failed completely. The insight was simple. The seven years that followed were not. I ask her about positioning, and the answer is a catalog of pivots. They started as a consumer marketplace—Masterclass over text, basically. Learn from professors at Harvard via your phone. “That model was just really hard to distribute,” she says. “Marketplaces are just really difficult, to be honest. Not really good for a medium that people didn’t already believe in.” A former chief learning officer told them about the billions spent on corporate training that drove zero results. They pivoted to corporate learning. Spent two years selling to HR. Got traction—then the market shifted. Enterprise budgets contracted in 2021 and 2022, and HR was the first department cut. “It was kind of a forcing function for us to find a better buyer,” Maxine says. They started selling to operational leaders. Sales directors. Frontline manufacturing managers. People whose bonuses depended on whether their teams improved. The product hadn’t changed much. The positioning had changed completely. I tell Maxine this is the part of product strategy that I think most product leaders miss. It isn’t about filling up a backlog and deciding which features will close deals. It’s figuring out what game you’re playing. There’s a great piece—I think it’s an a16z blog—about how the market is the most important thing. You can change your positioning and your target segment and sales go up. You don’t have to add more features. “Yeah,” she says. “We’ve had to iterate on our positioning a lot.” She describes what it’s like to sell without a category. Not just positioning on a macro level—telling the market a new way of thinking about employee enablement—but positioning per account. Every conversation is a custom pitch. Every buyer needs to understand something that doesn’t map to any existing line item in their budget. “For a while it was hard to lead product,” she admits. “We’re selling all these different use cases yet we don’t want to productize those pathways. We’re not a sales enablement tool. We’re not trying to compete with HighSpot directly. We’re really good for this part of sales enablement, this problem that’s not solved.” I bring up Figma as a parallel. How long it took for Figma to convince designers to switch from their existing tools. How category change requires not just a better product but a change in default behavior. “It did take a long time for Figma to get traction,” she agrees. “They had to change people from their default behavior of going to other tools as a solution.” The conversation moves to roadmap, and Maxine lights up. “There’s this quote that I love,” she says. “Plans are useless, but planning is useful. And I feel like that’s really true in a startup.” She describes the trap she sees product managers fall into: optimizing for delivery. Presenting a roadmap, hitting dates, feeling the satisfaction of shipping what you said you’d ship. She says the feeling of executing on a plan is seductive—and often wrong. “A roadmap often becomes a ton of things people ask for instead of what you’re trying to build towards over time,” she says. “Some of our best features have been where it doesn’t feel good. We shipped this a little too early, or we shipped this to see if we could market it. Or we marketed this five months early and built it in a funny way.” This is the part where most product conversations would veer into framework territory. Maxine stays concrete. She describes how she segments her roadmap into three buckets: what they’re working towards building, what they’re trying to build to convince people, and what they’re building because it’s literally blocking adoption at scale. “Those are the customer requests I take,” she says. “Literally, we would have five times volume if we shipped this feature. Not—oh, I would really love it if you could add this to a course.” She confesses they fell into the feature parity trap early. Customers would compare Arist to existing LMS products. The team spent six months adding features that mapped to what learning management systems already had—instead of building the fundamentally different thing they were supposed to be building. “What we’re building is fundamentally so different,” she says. “I have the agency in meetings with executives to say—that’s actually not our perspective. This is what we’re trying to build. This is what enablement should look like in five years, trust us. And it makes them back off a little bit.” That agency comes from conviction. Not confidence—conviction. Knowing what game you’re playing well enough to explain why certain features will never be built. Maxine tells me she spent significant time enabling the entire company on Arist’s vision. Not just the product team. Everyone. So that when a salesperson gets a feature request in the field, they can explain why Arist won’t build a one-on-one coaching product, and here’s why, and they will never build that, and here’s why. “Them being able to say those things is super valuable,” she says. “Because then you don’t get all these incoming requests of product to manage.” I ask whether finding the right buyer helped with breathing room for product. “Market is everything for product,” she says. Four words. No hedging. Finding the right buyer improved retention, simplified the roadmap, reduced internal pressure. It did what no process improvement or planning framework ever could: it gave product permission to build the right thing. Her co-founder, she tells me, is the one who holds the macro stance. “It’s very easy in a business to just really want the wins and explain things in ways people understand,” she says. “It takes a lot of positioning iteration to stick to the macro.” She mentions other companies in adjacent spaces that built text-message learning tools but positioned them as utilities for learning designers. They don’t see that learning designers won’t exist in their current form three years from now. They’re solving for today’s buyer in today’s category.

    54 min

  6. MAR 23

    #165 Richard Yu, CPO at LucidLink: Build Products That Disappear, Navigate High-Integrity Commitments, and Treat Strategy as a Hypothesis

    Richard Yu is the Chief Product Officer at LucidLink, where he leads product strategy for the company’s cloud-native file system used by distributed creative and enterprise teams worldwide. Rising to prominence in the 2010s as an enterprise SaaS product leader, he became known for building mission-critical platforms that turn complex workflows into scalable, repeatable systems. He is widely regarded for his focus on outcomes over output, pushing organizations to measure success by customer impact rather than feature volume. Previously, as Chief Product Officer at Formstack, he oversaw a no-code workplace productivity platform adopted by over 35,000 organizations across healthcare, financial services, and education. Under his leadership from 2022 to 2024, the company expanded its automation footprint across forms, documents, and e-signature workflows, helping customers digitize key processes end to end. He became known for driving cross-functional execution between product, marketing, and go-to-market teams to accelerate subscription growth and retention. His career highlights include serving as Senior Vice President of Product at Litmus, where he led a four-year stretch of category leadership that earned multiple G2 and TrustRadius awards for product adoption and customer satisfaction. Earlier, as Vice President of Product Management and Head of Product Management and User Experience at Marketo, he guided one of the world’s largest marketing automation platforms through a period when thousands of B2B organizations relied on it to orchestrate multi-channel campaigns. Across these roles, he has spent more than 25 years building teams, products, and businesses at the intersection of SaaS infrastructure, marketing technology, and data-driven customer engagement. Listen to this episode on Spotify or Apple Podcasts Learn how LucidLink’s “invisible product” design philosophy connects to Marty Cagan’s high-integrity commitments framework and why the best product strategies are testable assumptions, not finished artifacts. “We have users who experience it for the first time and kind of call it magic,” Rich Yu tells me. “So it is a bit magical, but obviously there’s no magic in technology. It’s just technology.” He says this with the calm of someone who’s heard the word magic a hundred times from customers and has learned to take it as engineering validation rather than compliment. Rich is the Chief Product Officer at LucidLink, and his product makes cloud-stored video files act as if they’re sitting on your local machine. You open your Finder, there’s a mount point, and the files are just there. Editors on The Bear scrub through footage with zero latency. No syncing. No downloading. No waiting. The company just won a technical achievement Emmy for this. And Rich’s philosophy for what comes next is to make the whole thing vanish. Richard Yu has spent 25 years in product and marketing leadership—Formstack, Litmus, Marketo—before landing at LucidLink, a cloud storage collaboration platform headquartered in San Francisco with an engineering office in Sofia, Bulgaria. The company powers post-production workflows for major streaming shows and found its product-market fit during COVID, when media teams went home and discovered that collaborating on large files remotely was, in Rich’s words, “just not tenable.” LucidLink solved that with streaming technology that caches intelligently enough to make remote files behave locally. The result is a product whose ideal user experience is one you don’t notice. I ask Rich what “it just works” actually looks like from the inside—because from a product design perspective, aspiring to be invisible is a strange thing. We spend our careers building interfaces, flows, and experiences that demand attention. Rich is trying to do the opposite. “We’ve really aspired to become invisible almost in the user experience,” he says. “I know that sounds ironic because as creators and builders of products, we always talk about what’s the user experience and what’s the UI look like.” He holds the irony for a beat. “But ultimately, if we’re thinking about the core value proposition—making large files stored in the cloud act and behave as if they were local on your machine—that’s something that should just happen.” I tell him about the declining weekly active users problem. A previous guest worked on translation software and discovered that as the product got smarter, people used the app less. For most teams, that graph is a crisis. For utility products, it’s proof of success. “Exactly,” Rich says. He gets it immediately. For LucidLink, the dashboard exists so administrators can manage permissions and check billing. But the actual value—the streaming, the speed, the absence of friction—that lives underneath everything. The best interaction is the one where a user opens a file, does their work, and never once thinks about the infrastructure making it possible. We drift into strategy, and Rich surfaces the question that shapes how he approaches product decisions: Are we building outcomes, or are we building outputs? He’s careful to credit the framework to others—”folks have blazed the path before me”—but the way he deploys it reveals conviction earned through experience. Early-stage companies need outputs. You need to ship the MVP, get it into market, learn. That’s the job. But once you have adoption and momentum, the game changes. “The value is what is typically called the outcomes,” he says. “Are users really using your product? Are they happy? Is there a community that’s excited and engaged? And then ultimately those outcomes are also company or business outcomes. Is the company growing and successful as a result of the customers being successful?” This connects to something else Rich is thinking about: the danger of high-integrity commitments. I bring up Marty Cagan’s framework—the idea that product teams should avoid locking into hard delivery dates unless the situation is truly existential. We’re going to lose this customer if we don’t ship. The business is under threat. Those are the only moments where committing to a specific scope by a specific date makes sense. Rich admits he falls into the trap himself. “As a product leader, I have accountability to my peers, to my executives, to kind of say, okay, we are gonna ship X by Y date,” he says. “I mean, that’s sort of one of the key anti-patterns in a way—that we are trying to constantly hit very specific dates with projects and initiatives that are not deterministic in that way.” He catches himself. “But I fall into that sort of trap myself because, let’s face it, in the business world, if we don’t have some forcing functions to get things done, work can fill up the space that it’s given.” The nuance matters. Deadlines aren’t inherently destructive. The anti-pattern is when hitting the date becomes the only thing you’re striving toward. When shipping replaces thinking. When the forcing function forces shortcuts in discovery, in design, in engineering. “It forces maybe shortcuts to be taken in the discovery and exploration and validation of that threat,” Rich says. “And then shortcuts taken in terms of the design and the actual engineering of the solution against the threat.” I push further: when you do make a high-integrity commitment, you need a team that believes in it. Not just one that executes against it, but one that owns it. “That’s where breaking down the silos across the three functions to creating this true triad ownership is critical,” Rich says. “The ownership in that high-integrity commitment is not engineering by themselves. It’s not design by themselves. It’s not product by themselves. It’s really all three.” The conversation turns to strategy and Rich offers what might be the most honest thing a product leader has said to me in 165 episodes of this podcast. “Any strategy, no matter how polished or how baked or how succinctly articulated—they’re just a set of assumptions and hypotheses,” he says. “Hopefully backed by sufficient data and research. But ultimately it’s a thesis. It’s a thesis until you’ve actually achieved the outcome that the strategy is trying to point towards.” I’ve watched the anti-pattern play out in real time. A product leader presents a strategy. The team pushes back. Instead of engaging, the leader hedges: Well, it was more of a thesis. A work in progress. They were hedging to save face. But Rich is saying something different—he’s saying all strategy is thesis, and that’s not a weakness. It’s how the work actually gets done. “I’ll go on a limb that even the smartest strategists out there, the most successful folks in technology, are probably always just running one or two steps ahead of reality,” he says. “And they’re trying to really figure things out.” He reaches for the scientific method. Hypothesize. Test. Verify. Iterate. It sounds basic—cliché, even. But his point is that the discomfort most product leaders have with strategy isn’t that they’re doing it wrong. It’s that they haven’t accepted the nature of the work. Strategy is a hypothesis you test with product decisions. The roadmap is the experiment. The outcomes are the data. “I really believe that strategy is formed in that cauldron,” he says. “Product roadmaps are formed in that cauldron. And great products are built using that sort of scientific method.” There’s one more thing Rich keeps circling back to, and it might be the connective tissue between the invisible product and the hypothetical strategy. He describes how his teams do quarterly reviews to examine the assumptions they made when deciding to prioritize, build, and ship specific features. Did we achieve the user outcomes we assumed? Did those outcomes ladder up to the

    52 min

  7. MAR 19

    #164 Chris Silvestri—AI Produces Great Stuff, If You Have a Process.

    Chris Silvestri is the Founder at Conversion Alchemy, where he helps B2B SaaS teams engineer message–market fit across web, sales, and email. Rising to prominence in the early 2020s, he became known for combining deep customer research, UX thinking, and decision-making psychology into scalable messaging systems that lift conversions rather than isolated campaigns. His work positions him as a widely regarded specialist for post–Series A SaaS companies seeking clarity, differentiation, and measurable revenue impact. Previously, as Founder & Conversion Copywriter at Conversion Alchemy, he led projects that generated up to 30% more qualified demo requests by clarifying value propositions and sharpening differentiation on 20+ core website pages and sales assets. He became known for shortening sales cycles by an estimated 15–20% by making value obvious earlier in the buyer journey and aligning messaging with actual customer priorities. His systems consistently drove 10–15% lifts in trial-to-paid conversions while improving internal alignment across marketing, sales, and leadership. His career highlights include serving as Conversion Rate Optimizer and UX Designer at Zeda Labs LLC from 2018 to 2021, where he blended qualitative research and experimentation to improve funnel performance and user experience over 2.5+ years. Earlier, he spent nearly a decade in engineering and industrial automation, experience that shaped his systematic approach to messaging, process design, and experimentation. Since 2020 he has also contributed to Good Product Club, writing on product strategy, UX, and go-to-market for teams building in an AI-driven world. As host of the Message-Market Fit Podcast, he helps B2B SaaS leaders understand how to translate customer insight into narratives that win deals and defend pricing power. Through his Unpacking Meaning newsletter, he publishes weekly breakdowns of SaaS messaging, UX, and buyer psychology for an audience of founders, CMOs, and growth leaders. Listen to this episode on Spotify or Apple Podcasts What a software engineer turned copywriter learned about positioning—and why 70% of the work happens before you write a single word. “If you don’t have a process, AI is gonna produce crap,” Chris tells me. “If you have a process, AI is gonna produce good stuff.” He says it like it’s obvious. Like the whole discourse around AI and creative work has been missing the point. Chris Silvestri spent ten years as a software engineer in industrial automation in Italy before transitioning to copywriting. He moved to the UK, founded Conversion Alchemy, and now helps B2B SaaS companies find message-market fit. He writes for Every. He’s not worried about being replaced by AI. But he has thoughts about who should be. I ask him to break down what he means by process. “First do the research,” he says. “Then don’t feed all the research to AI and have it write—or sometimes they don’t even feed the research and just ask it to write, which is even worse.” He pauses to let that land. “Use the research, distill it into your strategy, and then use the strategy as context for the LLM. So they can actually make sense of the data better.” This is the part most people skip. They dump raw transcripts and survey results into ChatGPT and expect positioning to emerge. But the synthesis—the actual thinking about what the research means—that’s human work. The AI can help you write after you’ve decided what to say. “Seventy percent of the work to me is research,” Chris says. “And then the messaging and the copy almost write itself.” I stop him. I want to make sure I understand the claim. He’s saying the writing is almost incidental? He nods. The hard part is everything that comes before. Chris’s engineering background shows up here. He sees messaging as a system with distinct layers. Positioning defines who you are. Messaging is how you articulate that across contexts—sales calls, landing pages, email sequences. Copy is the final layer, the actual words. Most people try to fix copy when the real problem is upstream. No amount of AI-generated headlines will save you if nobody agreed on what you’re saying in the first place. “A lot of times different departments don’t really agree on what they do better or differently,” he says. “And so then everyone starts kind of saying different things.” The jargon-stuffed copy that plague B2B websites? That’s not a writing problem. It’s an alignment problem. I ask about how he approaches customer research when the data is thin. Early-stage companies often don’t have enough customers to build detailed personas. “I think it’s useful to start with an archetype of your customers,” he says, “rather than saying, okay, this is a specific persona.” He explains the distinction. An archetype is a representative of a group—business buyer versus technical buyer. Under the business buyer archetype, you might eventually differentiate between CMO, CFO, and procurement. Under technical buyer: CTO, data engineers, developers. But if you’re early, you don’t have the data to specify that precisely yet. “We weren’t clear,” he says, describing a recent project with a data integration company. “So instead of crafting these ideal customer personas, we drafted these early customer personas. Business side, technical side. And from there we could move forward and get more specific.” Personas come later, when you have crystal-clear data on psychographics, demographics, decision-making patterns. Archetypes let you start building without pretending to know more than you do. This matters for AI workflows too. If you’re prompting an LLM to write for a persona you’ve fabricated from guesswork, the output will feel hollow. But if you’ve done the research—if you’ve actually talked to customers and heard how they describe their problems—you can give the AI context it can work with. “The more you compartmentalize your tasks in LLMs, the better it works,” Chris says. “I don’t even use ChatGPT or Claude for writing directly. There are loads of third-party tools that let you plug into the APIs without that pre-training those commercial interfaces have.” He’s building his own stack. One tool for finding signal. Another for working through strategy. A third for writing with his editorial style guide. Each chat stays focused. The synthesis happens in his head, not in the model. Near the end of our conversation, I ask what led him to embrace AI when so many writers are defensive about it. “I think first it was actually feelings of never being good enough,” he says. Something shifts in his voice. “Maybe it stems from the fact that I’m a non-native English writer. I’ve always said, what if I could be better? And then I saw AI, and now the playing field is level for anyone.” He decided to try every tool he could find. Learn what actually works. Keep up with the changes happening every week. But what he discovered surprised him. “Once you have a very specific and systematic process, AI can only amplify that.” The people most equipped to leverage AI are the ones who invested in their own brains before these tools existed. They have vocabulary. They have frameworks. They know what good looks like. Chris writes for Every now. He mentions how working with their editors makes him see things from a different perspective. The writer has one job. The editor has another. You try to mirror that same workflow when working with AI. “The craft, the taste,” he says. “That just makes you better and amplifies your ability to do more with AI.” I’ve been thinking about this since we hung up. The fear around AI in creative work is often misplaced. The tools don’t threaten people with strong processes—they expose people without them. Seventy percent is research. The rest is finding the right combination of insights, framing, and context. If you’ve done that work, AI is just another tool in the kit. If you haven’t, it’s a mirror. The Way of Product w/ Caden Damiano is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to The Way of Product w/ Caden Damiano at www.wayofproduct.com/subscribe

    51 min

  8. MAR 16

    #163: Mustafa Kapadia—You're Gonna Need More PMs, Not Less: The Counterintuitive Future of Product Management in The Age of AI

    Mustafa Kapadia is the Managing Director at Echo Point, where he helps product organizations use AI to eliminate operational drag and compound product velocity. Rising to prominence in the 2010s at the intersection of digital transformation and DevOps, he became known for translating emerging technologies into operating models executives could actually run. Today he is widely regarded as a leading advisor to product leaders seeking to turn generative AI into durable leverage rather than surface-level experimentation. Previously, as Global Head of Products & Innovation for Generative AI at Google, he led efforts to help the company’s largest enterprise customers, representing roughly the top 20% by scale, build new products and experiences on modern cloud and AI infrastructure. In that role from 2019 to 2023, he built new global innovation labs, combined sales and P&L ownership with hands-on product advisory, and drove adoption of generative AI across complex, multi-billion-dollar portfolios. He became known for helping Fortune 500 executives move from slideware to shipped product by redesigning how cross-functional teams discovered, validated, and launched new offerings. His career highlights include a seven-year run at IBM, where he grew an internal DevOps capability 3x into a market-facing advisory practice and later led the North America Digital Transformation practice. From 2012 to 2014 he built a cloud automation service that delivered double-digit growth while helping large enterprises compress infrastructure delivery from months to days. Earlier, he served on the Board of Directors at the DevOps Institute from 2015 to 2019, shaping curriculum and thought leadership as DevOps moved from niche practice to mainstream mandate in organizations managing hundreds of applications and billions in IT spend. He also co-founded Science4Superheroes in 2014, running it for eight years to introduce scientific thinking to children under five through playful, family-centric programs. As host of the Masters Of Product podcast and author of the AI Empowered PM newsletter on Substack, he helps more than 2,000 product managers each year learn to convert AI from a curiosity into a core part of their craft. Through private workshops, public cohorts, and consulting engagements, his work routinely unlocks multi-thousand-hour annual savings per organization and resets how product teams think about judgment, speed, and quality in the AI era. Listen to episode 162 on Apple Podcasts↗ and Spotify↗ Building gets easier. Deciding what to build gets harder. Here’s how the top 1% are preparing. “I had to figure out what I wanted to be when I grow up.” Mustafa Kapadia says this quietly, almost to himself. He’s describing the moment two years ago when he left Google—after 20 years at places like IBM and Google, running accelerators, building consulting practices, watching digital transformations succeed and fail. And then he walked away to help product managers stop being terrified of the thing that might replace them. I ask him about the fear. The senior engineers and PMs who’ve told me they’re just... opting out. Done. Can’t adapt. Won’t try. “I think we have really two camps,” he says. He holds up two fingers, almost making the “peace sign”—then stops. “Well, three camps.” Camp one: the AI-first believers. They start every task with an LLM. They use ChatGPT for one thing, Claude for another, Gemini for a third, NotebookLM for synthesis. They’ve rebuilt their entire workflow around what AI can do. Camp three: the skeptics. They want AI at arm’s length. Afraid it’ll outsource their thinking. Afraid it’ll take their jobs. They’re the same people who resisted mobile phones, who pushed back against the internet, who had concerns about every new technology since the printing press. And then there’s everyone else. The 60% in the middle of the bell curve, trying to figure out which way to go. “They want to use AI,” he says of the middle camp. “But they don’t really know how. They’re doing surface-level stuff.” Surface-level. He has a phrase for this. He calls it “using a Ferrari as a paperweight.” Most PMs use AI for three or four tasks. Summarizing documents. Writing emails. Maybe a little brainstorming. They’ve been handed one of the most powerful tools ever created, and they’re using it to check boxes. The top 1% do something different. I’ve felt this myself—the gravitational pull of the easy path. Voice dictation made it so simple to just talk through everything with Claude. I found myself reaching for AI before I’d even tried to think. At some point I started looking for a “brick” for AI, the same way I use a physical lock to keep myself off my phone apps. I tell him this. Maybe I should get my notebook out first, I say. Try to get as far as I can before— He cuts me off. Not rudely. Precisely. “You’re still using AI,” he says. “It’s just a matter of how you’re using AI. Depends on your comfort level.” Some people think things through first, then use AI to refine their thinking. Others start with AI—”just give me all the options”—then choose the ones they care about, move forward with their own thinking, then use AI to refine it again. Their thought process is sandwiched between AI. I ask him if there’s a right way. “I don’t think there’s a right or wrong way,” he says. “I think the more important question is: does it help you become more creative, effective, innovative as a product manager? And if the answer is yes—then more power to you.” He has a framework. Of course he does—he’s a consultant. But when he describes it, it sounds less like a sales pitch and more like a craft. “Five keys,” he says. “Assign a role. Provide first-principle inputs. Give it instructions—best practices. Format. And then an example that ties it all together.” The example he uses is user stories. You don’t just ask AI to write them. You prime the engine. You tell it: you’re world-class at this. You give it the problem, the user, the benefit, the feature. You tell it what a good user story looks like—customer-focused, unique, technical-free. You show it one. “And then—” he pauses. “Even if AI gives you ten great user stories, you don’t take all ten.” This is where it gets interesting. “You take the one or two that resonate. You use your own PM thinking. Your own experience. Your own context.” He calls this human-AI optimization. You’re not outsourcing your thinking. You’re using AI to prime you—to surface options you might not have considered. And then you decide. The middle 60% outsource their thinking. The skeptics avoid AI entirely. The top 1% sit in the tension between—augmented, not replaced. The conversation turns to something stranger. Synthetic personas. Mustafa is working with a client who has years of market research sitting on laptops and servers. Interviews. Surveys. Behavioral data. All of it gathering dust in slide decks nobody opens. “How do you take that research and make it actionable?” he asks. “How do you give it to someone in sales, or marketing, or product?” His answer: build a synthetic user. A simulated persona trained on all that research. Something a salesperson can practice objection-handling with. Something a PM can ask, “What would you think if we priced this at $99 instead of $149?” “It doesn’t replace talking to a real user,” he clarifies. “But in those crazy questions you want to ask—it’s a great way to refine your thinking.” Then he goes further. “We have a client who’s building a synthetic competitor.” I stop him. “A what?” “A synthetic profile of their competitor. So they can think about second-order effects.” He’s more animated now. “If I drop my price, what is this competitor going to do? If I launch this feature—a feature they already have—how are the two comparing? What can they do to make my feature less valuable in the marketplace?” None of this means it’s exactly what the competition will do. But it forces you to think. To make better decisions. You can run war games now that were never possible before. I ask him about the skeptics. The 20% who won’t get on the bus. What happens to them? He doesn’t sugarcoat it. “The ship has sailed,” he says. “The train has left the station. Whatever analogy you want to use—it’s happening. The only question as a PM is: where do you want to be? In the driver’s seat? The passenger seat? Or in the caboose, being dragged?” But then his tone shifts. Softer. Almost conspiratorial. “If you’re a PM and you’re ambitious—and most PMs are, which is why I love them so much—this is the best time to differentiate yourself. Organizations are dying for PMs who can show an AI-first mindset. They just don’t know what that looks like.” He’s not selling anymore. He’s confessing. “I prefer not to talk about what good looks like. I prefer to show them. Because until you actually show someone what a good PM with AI can do—that’s when they say, ‘Okay. How fast can we move?’” One client started with four or five AI use cases. After his team helped them understand what was possible—what the top 1% actually do—they identified over 250. That’s the gap. That’s the opportunity. Near the end, he says something that surprises me. “I think you’re going to need more PMs, not less.” I must have looked skeptical. “When you can build anything,” he explains, “deciding what to build becomes a much tougher decision. Building is going to get easier and easier. But figuring out what to build, what not to build, working with the business to determine what’s actually going to make an impact—that’s the job. And I think we’re going to need more people doing it.” The order-taker PM—business decides, PM translates, engineering builds—that

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The Way of Product is a philosophy magazine disguised as a podcast. Every week I publish two conversations with people who build in technology and product. Each one comes with a narrative essay that puts you inside the conversation through my eyes — what surprised me, what I kept thinking about after we hung up, where the principle actually lives once you strip away the jargon. I don't hand you the answer. I put you in the room and let you find it yourself. www.wayofproduct.com

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