The Way of Product with Caden Damiano

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 PodcastsThe 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

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 PodcastsDiscover 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 solving this puzzle?”Her framework for puzzle-setting uses three O’s: Observation, Open Questions, and Objective. The observation captures what’s actually happening, not just what the metrics show. The open questions identify what the team doesn’t understand about the observation. The objective summarizes the puzzle that needs solving.For Avid, the observation would have been: “Our low end is getting eroded by Apple and Adobe in the mid-tier. This is what’s happening. The market is getting eroded. The way we’re making the numbers is by going further into the high end.”The open questions would probe deeper: “What is happening on the low end? Adobe and Apple are successful there. What is their business model? Can we fight this business model in a different way? Is there something we can offer that can be a complete workflow for the low end where even if Apple and Adobe are giving away the editor, people will want it and want to pay for it?”The objective becomes: “Figure out what do we do in our video editing business. Do we invest in it, do we not, or how do we invest in it, so that we can continue to either be successful in the video editing business, or we choose to move on and adapt our business?”This is puzzle-setting. It creates space for teams to investigate reality rather than optimize metrics. But puzzle-setting only works if teams have the skills and safety to solve puzzles effectively.That’s where puzzle-solving comes in: three questions that teams answer as they work on the puzzle. How well did it work? What did we learn? What will we try next?“Notice how this question, it’s not binary, did you or didn’t you hit this target? It’s not just putting you on the spot, making you feel like I have to prove something. It’s genuinely inviting the good and the bad. This is how as a leader, you’re not the last to know you’re inviting the good and the bad.”The second question—what did we learn—requires narrative synthesis, not just dashboard reporting. Teams have to look at all their data and tell the story of what’s really happening with users, markets, and competitors.The third question—what will we try next—forces strategic thinking based on actual learning rather than predetermined roadmaps.“I can really tell based on working with a team who is thinking deeply and how well they’re solving the puzzle based on their answers to what have we learned and what will we try next? That’s how you can evaluate people, not just based on ta-da, I’ve hit my numbers.”The transformation this creates in team dynamics is profound. Instead of competing to show green dashboards, team members compete to solve interesting problems. Instead of hiding bad news, they compete to surface the most important insights. Instead of gaming metrics, they compete to design better experiments.But this approach requires a level of psychological safety that’s rare in most organizations. Teams have to be willing to admit what’s not working, leaders have to be willing to hear it, and everyone has to be willing to change direction based on what they learn.“Did you know that he didn’t keep a corner office? He used to have a cubicle, same size cubicle as everyone else because he wanted everyone to challenge his ideas and to feel like they could speak up. Very few leaders want people to speak up and tell them this is not working.”The Andy Grove reference isn’t accidental. Grove understood that organizational hierarchy creates information distortion. The further you are from the work, the more filtered your information becomes. Physical proximity—sharing the same kind of workspace as everyone else—was one way to counteract that distortion.Most leaders won’t give up their corner offices. But they can start role-modeling the kind of reflection and transparency they want from their teams. Taking time in meetings to discuss what didn’t work in past initiatives. Sharing their own learning and uncertainty. Creating space for teams to investigate puzzles rather than just hit targets.“You can role model for your team, the psychological safety and sharing the good and the bad of what didn’t work, what you learned from it, what you’re going to try next. You can role model this so that you can invite the team to solve puzzles like you are.”For individual contributors stuck in goal-driven organizations, Radhika recommends starting small. Take a past feature release and work through the three puzzle-solving questions privately. Look at the data, but focus on the narrative: what really happened with users? What did the numbers mean in context? What would you try differently next time?Once you’ve practiced this approach yourself, try it in one-on-ones with your manager or conversations with peers. Create small bubbles of psychological safety where honest reflection and learning can happen.“Instead of just chasing OKRs, you’re working on puzzles. Puzzles are so much more fun. We are all energized by puzzles. Instead of just focusing on OKRs, think about what puzzles you’re solving for the company. That in itself will energize you for your work.”The energy difference is real. Goals feel imposed—something you have to hit to prove your worth. Puzzles feel intrinsic—something you want to solve because the solution creates value. The shift from external validation to internal motivation changes how people approach their work.But the business results matter too. Radhika’s recent consulting engagement provides a concrete example. A company stuck with stalled sales in 2023 doubled sales in 2024, then doubled again in 2025 after switching from goal-setting to puzzle-solving. Customer churn dropped from 26% to 4%.“We did all of that by puzzle setting and puzzle solving instead of being driven by OKRs.”The transformation didn’t happen overnight. It required leaders willing to let go of familiar frameworks, teams willing to embrace uncertainty, and everyone willing to prioritize learning over looking good.The alternative—continuing with product diseases like hero syndrome and hyperemia—leads to the dating app outcome. Short-term metrics that mask long-term erosion. Features that optimize for engagement instead of value. Teams that hit their numbers while slowly destroying what they’re trying to build.“Or are we all doomed to just constantly learning from these failures, making mistakes and having to learn the hard way?”That was the question that drove Radhika to develop radical product thinking in the first place. After watching team after team catch the same diseases, make the same mistakes, and suffer the same consequences, she wanted to understand whether systematic approaches could prevent predictable problems.The answer is yes, but only if teams are willing to diagnose their diseases honestly and treat them systematically. Most organizations prefer to treat symptoms—choosing better metrics, writing clearer requirements, running more experiments—rather than address root causes.The root cause is the gap between great ideas and great products. Steve Jobs called it out in his lost interview: most people think the idea is 90% of the work when it’s actually 5%. The other 95% is the systematic translation of vision into strategy, strategy into priorities, and priorities into daily activities.“And I think filling that gap is exactly what I talk about in terms of systematically translating a vision for change into action, into everyday activities. And that’s how we close that gap.”Product diseases spread when teams try to shortcut that translation process. Hero syndrome emerges when teams skip from big vision to scaling without defining the problem. Hyperemia emerges when teams skip from activities to metrics without understanding the connection to long-term value.The systematic approach isn’t glamorous. It requires detailed problem statements, clear frameworks, consistent reflection, and honest measurement. It requires admitting when things aren’t working and changing direction based on learning rather than predetermined plans.But it’s the difference between revolutionary wireless and amateur wine drinkers who can’t find wines they like. One vision launches a company that doesn’t know what it’s doing. The other launches a company that gets acquired because it solves a real problem in a specific way.“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.”That clarity—knowing exactly what you’re doing and why—is what prevents product diseases from taking hold. It’s what enables teams to choose long-term value over short-term metrics. It’s what transforms abstract strategies into concrete progress.The vision template is just the beginning. The systematic framework for translating vision into action is what makes the vision matter. And the puzzle-solving approach is what keeps teams connected to reality as they execute against the vision.Twenty-five years after revolutionizing wireless, Radhika has learned to revolutionize something more specific: how product teams think about the problems they’re trying to solve. Not with better tools or processes, but with better questions and frameworks for finding answers.The questions aren’t complicated. What problem are we solving? Why does it need to be solved? How will we solve it? How well is our solution working? What are we learning? What will we try next?The complexity comes from creating organizational conditions where teams can ask those questions honestly and act on the answers systematically. Where puzzle-solving is rewarded over performance theater. Where learning from failure is valued more than hitting arbitrary targets.“Puzzles are so much more fun. We are all energized by puzzles.”That energy—the intrinsic motivation to solve interesting problems—might be the strongest antidote to product diseases. When teams are genuinely curious about the puzzles they’re solving, they’re less likely to settle for b******t statements that are slim on details. They’re more likely to demand the clarity that prevents revolutionary wireless from becoming just another failed startup story.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

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 PodcastsLearn 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 manage AI agents effectively can take on exponentially more responsibility. People who don’t learn these skills find their scope of influence shrinking as AI-augmented colleagues outpace them.“Some people will use the time that they get back with AI to just do more of what they already know, and that’s gonna be fine,” Saurabh observes. “But you’re gonna have other people that are able to—I sometimes think about Maslow’s hierarchy. Some people that are able to, okay, great, I got shelter and food under control. Now I can go to self-actualization.”The Maslow reference isn’t casual. It’s how he thinks about organizational development in an AI-augmented world. Some people will use AI to get better at their current job. Others will use AI to access entirely different kinds of work. The first group maintains their position. The second group expands their influence.But this creates new challenges for team composition. How do you balance strategic thinkers who can direct AI agents effectively with craftspeople who can execute at high quality? How do you maintain institutional knowledge when so much tactical work gets delegated to machines?“There are exceptional middle managers that that’s what they love to do. That’s what they’re good at, and that is great,” Saurabh says when I ask about the career implications. “And then there are exceptional middle managers that graduate naturally to be exceptional executives. And that is good as well.”The key insight: both paths remain valuable, but the skills required for each path are changing. Middle managers will increasingly manage hybrid teams of humans and AI agents. They’ll need to be excellent at coordination, quality control, and tactical execution within defined boundaries. Executives will set those boundaries, choose which problems deserve attention, and build conviction around uncertain directions.But the boundary between these roles is becoming more porous. When AI handles routine analysis, middle managers can engage in more strategic work. When strategic insights can be tested rapidly, executives can stay closer to tactical details. The rigid hierarchies built around information scarcity start to flatten when information becomes abundant.“Where is that compounding advantage that creates value for the customer and also creates potentially a competitive moat for us as well,” Saurabh concludes, returning to the core question.The answer, increasingly, isn’t in what you can build. It’s in what you choose to build and why. The technical capability to create software is becoming commoditized. The judgment to create the right software at the right time for the right customers remains scarce.Companies that hire for execution speed will compete on efficiency. Companies that hire for strategic judgment will compete on alpha. Both approaches can succeed, but they require different organizational designs and different definitions of performance.The retailers who miss the next pickleball trend won’t be the ones with outdated technology stacks. They’ll be the ones who couldn’t distinguish between signals worth pursuing and noise worth ignoring. Who couldn’t move fast enough from insight to action. Who optimized for doing more of the same instead of doing something different.“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.”As AI makes more capabilities available to everyone, the companies that thrive will be the ones that focus obsessively on the capabilities that can’t be commoditized. Not because they’re technically difficult, but because they require the kind of human judgment that compounds over time rather than getting automated away.The question for every product organization: are you hiring people who can do the work, or people who can choose the work? The first skill set has a shrinking shelf life. The second becomes more valuable every sprint.Subscribe 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

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 PodcastsThe 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 on thinking about what problem can be solved.”The solutions will come. They always do. The hard part—the part that separates a Target beacon from a Taelor, a failed eBay listing tool from a 10-million-impression marketing flywheel—is choosing the right problem in the first place. Not the coolest one. Not the most technically interesting one. The one that actually matters to the person on the other end.Selection criteria over ideas. Every time.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

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 PodcastsHow 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. Arist is building for a category that doesn’t exist yet.“It does require someone who takes the right macro bets,” Maxine says. “Which you need someone who can do that well.”I think about Linear’s five-year slide—year one is friends, year two is small startups, year five is enterprise. The CPO who defaults to no on dashboard requests because they’re counter-positioning against Atlassian. The clarity that comes not from better planning but from sharper conviction about who you’re building for.Maxine and her co-founder have that clarity. It took seven years of positioning iteration, a near-shutdown, a global pandemic, and the courage to walk away from the HR buyer. But they have it. And the roadmap, as she predicted, is taking care of itself.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

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 PodcastsLearn 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 business outcomes they were supposed to?“This is hard to do,” he admits. “Because you can always say it didn’t happen because we didn’t market it correctly, we didn’t sell it well. There are lots of mitigating factors. Nonetheless, I think it’s really important for all the product triads to hold themselves accountable.”The anti-pattern is top-down mandates. When strategy flows from a single leader and everyone else is just executing, accountability evaporates. “Oh, we built this because Rich told us to do it,” he says, narrating the dysfunction. “And then if it doesn’t work out, it’s not anybody’s fault but Rich’s or Rich’s boss.”He calls it demoralizing and dysfunctional. But the word that sticks with me is erodes. Trust erodes when leaders mandate without inviting the team into the hypothesis. It erodes when outcomes are never checked against assumptions. It erodes when shipping is the metric and nobody asks what happened after the feature went live.Rich Yu builds a product designed to be invisible. He leads with a philosophy designed to be the opposite—transparent about what’s known, honest about what’s uncertain, accountable to what actually happens when the hypothesis meets reality. The invisible product. The visible strategy. The thesis that never pretends to be a conclusion.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

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 PodcastsWhat 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

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 role is dying. What’s emerging is the PM as decision architect. The one who navigates the infinite possibilities that AI unlocks and says: this one. Build this.He is not wrong. New computer science (CS) students are already doing this. My engineering manager told me recently that his son is in college, doubling down on AI education instead of a traditional CS degree. The homework is mostly about giving context, setting up system prompts. “This is basically PM work,” I said.Mustafa nodded when I told him this. It’s becoming a common observation. The engineers need product thinking. The designers need product thinking. Everyone’s developing the competency because the alternative—hiding behind tactical building, being a feature factory—doesn’t work anymore.We sign off. He mentions a benchmarking study dropping soon—fifty or sixty CPOs, data on how the best are actually using AI. He gives me his Substack. Echo Point.“I give away 95% of what I tell my clients for free,” he says.I believe him. And I subscribe before the call ends.The last thing I remember is him saying something about the middle 60%. How you don’t have to convert the skeptics. You just have to pull the middle toward the top. And once 80% of your organization is using AI for 250 use cases instead of four...The other 20% stops mattering.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

Matt D. Smith is the founder of Shift Nudge, a professional interface design training platform for working designers. Rising to prominence in the 2010s for his systematic approach to visual interfaces, he became known for turning over 20 years of interface design practice into a structured curriculum used by thousands of designers worldwide. His work on design patterns and tools has made him a widely regarded figure in modern interface design education.Previously, as founder of Shift Nudge, he built a global program that helps designers advance their careers in as little as 8–12 weeks while receiving mentorship and support for a full year, equipping them to lead teams and ship production-quality interfaces. He became known for transforming working designers’ income trajectories, with students reporting income growth of 2x within a few years by applying his methods in typography, layout, and spacing. Through Shift Nudge, he has trained designers from leading startups and global brands, positioning the program as a modern alternative to traditional design education.His career highlights include pioneering the Float Label pattern in 2013, a form interaction now adopted across products from Apple, Google, and countless consumer applications. He also created the interface design tools Contrast and Flowkit, Figma plugins that have reached tens of thousands of users and are used to check color contrast and design user flows inside modern design tools. Beyond product work, he has served as an adjunct professor at the University of Georgia and delivered workshops and talks at conferences including Adobe MAX, Dribbble Hangtime, Figma’s Config, Smashing Conference, and others, extending his influence from the classroom to stages across the United States.Listen to episode 163 on Apple Podcasts↗ and Spotify↗What a decade of design fundamentals taught me about delegating to Claude Code—and why Shift Nudge was secretly an AI onboarding course before AI existed.“I have a weird obsession with trying to get the absolute most difficult username across every platform,” Matt says, and it lands like a confession. He goes by MDS on the internet. Three letters. You can imagine the negotiations, the dead accounts, the patience required.We’re a hundred episodes into knowing each other—he was guest number 50, and now here we are past 150—and he’s still introducing himself as someone in transition. “I’m a designer turned educator now sort of turning into a CEO trying to figure out how to run a design education business.” There’s something in how he says trying to figure out that earns the pause that follows.I’ve watched Matt’s public work for almost a decade. I was the third beta tester to graduate from Shift Nudge back in 2020. I bought low, as I like to say. The course has appreciated since then, but so has something else—something I didn’t understand I was learning until AI came along.When Claude Code got good enough to actually help with design tasks, I noticed I could delegate effectively while other people couldn’t. The difference wasn’t technical skill. It was vocabulary. Every time I’d tell Claude to “adjust the row height” or “try a card component instead of a list view,” I was drawing from a library of concepts Matt had codified years earlier. Those concepts weren’t just design rules. They were the building blocks of clear instruction. The most valuable thing I learned from Shift Nudge was the vocabulary. When I became a design lead, I could articulate with precise vocabulary what wasn’t working in someone’s design. Subject, object, verb. The spacing is off for this reason. That precision made me good at delegating to humans. Now it makes me good at delegating to machines in the form of Skill files to AI agents. Matt nods slowly. “Skill files,” he says, “they’re good at getting directionally correct, especially things that are like absolutely binary. Is this the way you write an HTML link or is it not? It’s definitely right and wrong.” He pauses. “Whereas design... there’s more gray area than black and white.” Capturing the nuance a missed in my observation. He’s talking about Claude’s skill files—those markdown documents that give AI context about how you want things done. And he’s right that they work best for the binary stuff. But here’s the connection he helped me articulate: skill files are functionally identical to the Standard Operating Procedures you’d write for a junior designer.I bring up The Defiant Ones, the documentary about Dr. Dre and Jimmy Iovine. When Jimmy was learning to be a record producer, his mentor taught him by working through him. “Adjust the reverb. What happened there? Why did that work? Why did that not work?” It’s the master-apprentice model, I say. And I think that’s where things are going with AI.Matt leans into it. “You still need that institutional knowledge, the vocabulary. AI can adjust the reverb and adjust the echo and adjust the panning. Oh, you want five different beats? But it’s like—why? How much? When do we stop?” He lets the questions hang. “That creativity... I think there’s gonna be, you know, in the same way that there was a big resurgence of live in-person things after COVID—I think we’re all gonna be like, it’s just refreshing when I read something online and I can tell that a human wrote it.”There’s something in his voice when he says refreshing. Like he’s already tired of the alternative.I ask about the divergence he sees coming—who wins, who loses. He doesn’t hesitate.“There’s gonna be a divergence where the person who doesn’t use AI is just simply not as effective as the person who learned how to use it. But then there’s also gonna be a divergence of—I’m using AI all the time and this other person is like, well, I learned a lot of things before AI existed and I use AI and now I know more than you.” He pauses. “And this other person’s just fully reliant on AI and they don’t know much.”It’s gonna be harder to learn things, he says, because AI is so instant. “It makes it like painful to sit down and read something and actually learn it yourself.”The irony is that the people most equipped to leverage AI are the ones who invested in their own brains before these tools existed.Matt has a framework for mapping where you fit in all this. He calls it Pioneer, Builder, Consumer.Pioneers are the people at Anthropic and Cursor and OpenAI—building the intelligence and the harnesses that give it to us. Builders are the developers and designers using these tools to create products. “We’re sort of converging slowly,” he says. “Designers are over here and developers over here, and some are still better at infrastructure and setup and code—like, oh, that’s why would you use useEffect here in React—and designers over here like, what does that mean? But it’s starting to be irrelevant because some of the tools are getting so good.”And Consumers? “My mom is a good example,” he says. “She’s not choosing to have AI in her life. She’s just seeing it happen through Amazon review summaries or Google AI summaries for the things that she used to search for.”The question isn’t whether AI will touch your life. It’s which persona you want to occupy.I push on the vibe-coding hype. All those people on Twitter saying software is cooked because they built a Facebook clone in five minutes.“I don’t wanna rely on your janky vibe coded app to help me,” Matt says, and there’s a dry humor in it.I have a follow-up I’ve started using. Whenever someone says “I did this with AI”, I ask: Cool. So what’s your plan to maintain it?They never have an answer. That’s when you realize why we pay engineers. DevOps, infrastructure, support tickets—that’s the unglamorous work that keeps the train running. Building something on your own is a lot different than supporting a hundred thousand users at once.Near the end, Matt gets reflective about advice. “You’re gonna need your own knowledge,” he says. “Build that vocabulary through any means possible. Whether it’s asking questions from AI while you’re learning, or watching videos, or attending school. I think there’s still real value in you building your own brain.”He catches himself. “And if you don’t want to do it—you know, maybe you change careers. I don’t know.” Something shifts. The pragmatism cuts through.“Just kind of plot yourself,” he says. “Are you a pioneer? Are you gonna be a builder? Are you just gonna be a consumer? Because either way, AI is gonna be touching a part of your life, whether you choose to or not.”I’ve been thinking about this since we talked. I’m reading books again—not AI books, the fire hose has enough of those. I’m building vocabulary in domains outside tech: marketing, strategy, positioning. The cost of building has collapsed. The cost of deciding what to build has not.Everyone with taste is not in tech right now. It’s in the humanities, philosophy, long-form content.That’s where I’m looking.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

Steph Cartwright is a Job Search Strategist and Certified Professional Resume Writer (CPRW) at Off The Clock Resumes LLC, where she helps tech and industry leaders present as confident, high‑value candidates on screen. She became known for career branding that turns complex experience into clear, employer‑ready narratives that consistently convert views into interviews. She has built an audience of more than 3,200 followers and over 500 direct connections while operating from the Spokane–Coeur d’Alene region.Previously, as Founder and Principal Writer at Off The Clock Resumes LLC, she scaled a boutique career services practice into a specialized partner for job seekers navigating competitive roles with compensation packages frequently exceeding six figures. She became known for a structured, data‑driven intake process that translates into résumés and LinkedIn profiles optimized for modern applicant tracking systems, significantly increasing interview rates and offer quality for her clients. Through one‑to‑one engagements and digital products, she has supported hundreds of professionals across tech and adjacent industries.Her career highlights include earning and maintaining the CPRW credential, signaling adherence to rigorous professional standards in résumé writing and career communication. She has continued to refine a distinctive positioning around “career branding that gets noticed and lands interviews with higher offers,” focusing on clarity of story, on‑screen confidence, and repeatable systems that scale beyond any single job search. By combining structured frameworks with empathy for career pivots, she has become a trusted partner for leaders who need to articulate complex trajectories in two pages or less.Listen to episode 161 on Spotify or Apple PodcastsWhy the old keyword-stuffing playbook is dead. And what job seekers should do instead.“I am the face behind my business and in front of my business,” Steph says, “as well as the one that does all the one-on-one work with clients.” There’s something in how she phrases it—face behind and in front—that captures the exhausting clarity of solopreneurship. She’s the product and the salesperson. The expert and the marketer. And she’s been doing it since 2014.She started as a serial job seeker. “I am well rehearsed in job search practices,” she says, with the kind of dry humor that only comes from having lived through too many of them. Now she’s getting ready to attend another annual conference to stay current on hiring tech. The landscape, she tells me, is shifting faster than most people realize.I ask her what current hiring practices are doing to block talented candidates.“It’s gone beyond applicant tracking systems,” she says. “That used to be very keyword based. And now it’s not so much the worry of making sure your resume has all the right keywords.” She pauses. “AI is now adding generative and predictive analytics to this technology. It’s actually going to make it easier for job seekers because they don’t have to worry so much about the specific keywords.”This is counterintuitive. For years, the advice was: mirror the job posting. Product development. Project management. Agile methodology. Match the strings, beat the algorithm, get in front of a human. That era, Steph tells me, is ending.She walks me through an example. Say a product developer five to ten years ago wanted to tailor their resume. They’d add the term product development to ensure their resume would surface in searches. If someone went into LinkedIn looking for that skill, they’d pop up. “It was really important to have the right keywords, the right phrasing,” she says.Now? “If you don’t have the specific words—the specific product development phrase—AI is going to look at your experience and it’s going to look at context. It’s going to look at, you know, predictive. If you say you’ve done this, you likely have this skill.” She lets that land. “AI is going to start making assumptions about you that will help you.”The old systems were deterministic. You could game them if you knew the rules. The new systems are probabilistic. They infer. They read between the lines. This is good news for generalists and career changers—people whose careers don’t fit neatly into keyword buckets.I tell her this resonates. I’ve jumped between design and product management throughout my career, and I’ve gotten direct questions: What do you actually want to do? Few people accept my honest answer, which is basically whatever the company needs and I find interesting at the time.Steph nods. “At some point in the last ten years, the trend shifted from wanting someone with a broad range of skills to: we want a specialist, we want someone who really is an expert in this one thing.” She pauses. “But now that we’re adding in this AI element, we’re kind of going back to the original trend where AI wants to see the breadth of your knowledge and then be able to say, yes, this person has these skills, but they also have these skills, which will likely be a good fit.”The conversation turns to how people market themselves, and Steph lands on an analogy that sticks.“Highlighting benefits over features,” she says. “Those keywords, those skills—those were features, not the benefits. Whereas now, if you shift your mindset to: I’m going to position myself as the best fit for this job, not because of my skills, not because of the features that I bring, but because of the impact I’ve made.”She explains how this plays out technically. “One bullet on your resume can speak to an ATS based on the keywords in it—so that one bullet may be associated with project management skills. Whereas now with AI, that one bullet, depending on how much information you give it, might register five, six, seven different skills associated with that one bullet because of the impact you had.”The example she gives: designing a product that increased efficiency for a large enterprise. That single bullet, written with context, signals project planning, project management, design, strategy—multiple capabilities inferred from one outcome. The question isn’t What can you do? It’s What have you made happen?I bring up LinkedIn, and how I’ve started writing narrative case studies instead of bullet points for each role. The bet is that AI will read it and extract more context to provide better evaluations to hiring managers.Steph lights up. “Storytelling, especially on LinkedIn, is key. I used to work with clients very specifically on, let’s take these bullets on your resume and expand them as projects on your LinkedIn profile. Because that project section is also searchable. It’s also readable by the tools behind the scenes.”She leans into it. “Tell me the full story. How it started. What was the challenge that needed to be resolved. What you did, who you impacted, what obstacles you faced, and then what was the ultimate outcome.” Each project gets 2000+ characters, she says—2000 characters the AI can read, infer from, match you to opportunities.But the real shift in her thinking, she tells me, isn’t about resumes at all.“If you don’t tailor your resume for this specific job before you apply, you won’t even be considered,” she says. “I am still a strong believer in tailoring a resume if you’re gonna apply online. But now, because the competition is so high, I would say it’s more important to have a full blown strategy built outside of applying for jobs online.”What does that strategy look like?“It’s more important to be strategic in who do I need to talk to? Who can I start relationships with—even if it doesn’t result in a job at that company—but is going to expand my reach in my targeted field or industry?”She reframes networking as something that makes people less uncomfortable. “You can’t just think of it as networking—just getting your name out there and hoping something lands. But building professional friendships is what is going to make the difference.”I ask her how she coaches someone who’s just starting out, someone without an existing network.“Find a trade or professional organization that you can join and actively participate in,” she says. “One that opens you up to develop professional friendships with people you would maybe look at as competitors for different jobs, but they’re also mentors.”She tells me about a colleague halfway across the country. “She and I just sat down and had lunch together over Zoom and just talked shop. She has sent me referrals. I have sent her referrals. I would call her a mentor, but we’re also friends.” There’s warmth in it. “I know she’s in my corner. She will never do something to jeopardize that professional friendship.”I share a story from my own career. Five years ago, I had an offer from a company that I turned down for something more interesting. The hiring manager was a class act about it—That sounds really cool, I’m really excited for you—and he kept in touch. For five years. Then, recently, when I was looking again, an opening came up. I interviewed. It went well. Then a budget issue threatened to kill it. Another team needed to shuffle a designer internally. I waited all weekend, assuming it was over.The recruiter called. We want you here. We have to work this out, but we really want to figure out a way to make this work. They talked to the VPs. Got budget approval. Carved a spot out for me.“That’s best case scenario right there,” Steph says.It’s a five-year story arc, I tell her. And it only worked because the relationship was real.“That is the end goal,” she says. “You’re not going to find that by just applying for jobs on Indeed. You have to do that extra work. And the narrative of this is how you’re supposed to find a new job keeps people from trying.”She pauses. “Companies are notorious for creating roles for the people they want. If you can figure out what that company’s challenges are and how you can help them solve those challenges—that’s what’s going to help you get your foot in the door at a company you’re gonna be happy with.”Near the end, she gets reflective. “I didn’t go into this thinking we were gonna deep dive on resumes or LinkedIn. I feel like we really covered a wide range of strategy.”She’s right. We barely talked about formatting or bullet points. We talked about the slow, patient work of being known. Of building something that compounds. Of treating your career like a series of stories worth telling.The basics still matter, she reminds me. A solid LinkedIn presence. A resume ready to go. But the game has changed. The people winning aren’t the ones optimizing keywords. They’re the ones showing up—at conferences, in communities, in DMs—building professional friendships before they need them.That’s the strategy that’s AI-proof.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