The Way of Product with Caden Damiano

Keith Lucas is a startup advisor specializing in product, growth, people, and culture who previously served as Chief Product Officer and Chief Technology Officer at Roblox, where he helped transform the platform into a global ecosystem for tens of millions of creators and players. Rising to prominence in the 2010s, he became known for building engines of innovation inside entrepreneurial teams, uniting long-term mission, values, and execution into a single operating system for high-output organizations. He is the author of Impact: How to Inspire, Align, and Amplify Innovative Teams, a 202-page playbook published in 2025 that codifies these practices for leaders across high-growth technology, gaming, and AI-driven companies.Previously, as Chief Product Officer and later Chief Technology Officer at Roblox, Lucas led the product and engineering organizations through one of the strongest multi-year growth runs in the company’s history, helping drive player and revenue expansion of roughly 300–400% year over year heading into 2016. He scaled the product organization from a single product manager and a small design and analytics group to a 30-person, data-driven team, while guiding engineering from bi-weekly releases to daily and weekly cadences across web and core client surfaces. During this period, he helped architect the platform’s shift to mobile-first growth, global game server distribution, and a more systematic approach to discovery and developer incentives, contributing to annual revenue that would later be reported in the billions of dollars as the company matured.His career highlights include serving as Chief Operating Officer at Instrumental, an AI-powered manufacturing intelligence company where he helped the business grow its customer base across consumer electronics, automotive, and medical devices as revenue expanded by an order of magnitude in the wake of its Intercept product launch. Over two decades in technology, he has held senior roles across engineering, operations, and business, from early-stage leadership at Roblox to advisory work with startups in AI, gaming, entertainment, and enterprise software, bringing a portfolio of experience that spans platform infrastructure, creator ecosystems, and go-to-market strategy. Lucas holds a Ph.D. in Aeronautics and Astronautics from Stanford University and a Master of Public Policy from the University of California, Berkeley, a combination that underpins his analytical approach to building enduring, institution-scale teams.As author of Impact: How to Inspire, Align, and Amplify Innovative Teams, he codifies a two-tier framework that helps leaders avoid stalled scaling, culture dilution, and loss of focus by treating culture as a system and leadership as a discipline. He now works directly with founders, CEOs, and executive teams as a trusted advisor, helping them design what he calls “engines of innovation” that can sustain compounding impact over decades rather than single funding cycles.Listen on SpotifyListen on Apple PodcastsInnovative teams do not stumble into great productsThey intentionally build engines of innovation in how they hire, promote, and operate day to day. Keith Lucas has seen both well run and badly run startups, and the pattern he cares about is deceptively simple: Purpose-driven companies that adopt a long-term, institution-building mindset have a structural edge over those optimized for short-term financial wins.​When Keith thinks about building entrepreneurial teams, he looks for five “non-negotiables”: * Can this person elevate the team’s ability to create, innovate, or solve problems?* Do they align with the values? Do they want the same long term outcomes?* Do they believe in the mission?* Can they live with the team’s non-negotiable principles?* Do they meet the minimum standards of mastery and autonomy?Teams that take those standards seriously quickly surface who needs too much handholding or who does not care enough about quality, because the realized culture will not support them.​Here’s a practical nugget you can take from this episode today (though I recommend you listen to the whole thing, it’s one of the best episodes on leadership)His favorite hiring and team staffing question for sussing out these non-negotiables is something I am going to steal: When you have a free moment at work, where does your mind go?The answer exposes intrinsic motivation, and great leaders use that signal to dial in roles so that enthusiasm, skill, and impact line up instead of grinding against each other.​Underneath all of this is a simple thesis: if you want an engine of innovation, you need people who behave like mission athletes—mission driven, performance oriented, continuously growing, and elevating their peers—and you need to give them aligned autonomy instead of micromanaged checklists. This episode is for builders who care about creating something enduring rather than chasing short-term wins, and who are willing to design their hiring, culture, and leadership practices to match that ambition.​ Get full access to The Way of Product w/ Caden Damiano at www.wayofproduct.com/subscribe

Matthias Wagner is the Founder and CEO at Flux, the AI-native hardware design platform streamlining how teams build printed circuit boards and electronics at scale. Rising to prominence in the late 2010s, he became known for transforming manual, spreadsheet-driven supply chain and PCB workflows into cloud-first, collaborative systems used by distributed engineering teams worldwide.Previously, as Product Manager at Facebook (now Meta), he led the Moments App, AR ads, and Oculus VR initiatives, working on products that collectively reached hundreds of millions of users globally. During his nearly three-year tenure, he operated at the intersection of machine learning, consumer-scale experimentation, and hardware-enabled experiences, which directly informed Flux’s AI-first approach to electronics design.His career highlights include co-founding 42media group in 2004 and bootstrapping it into a multi-million-euro digital signage and media business serving enterprise clients such as IBM, McDonald’s, DHL, Volkswagen, and major German financial institutions. He also co-founded Hochzeit.de, a wedding marketplace with planning and budgeting tools that grew into a leading German platform connecting thousands of couples with venues and vendors, and he has mentored multiple startup cohorts, helping dozens of founders move from idea to growth-stage businesses.Listen on SpotifyListen on Apple PodcastsShip platforms that make hardware iteration feel like modern dev tooling.Matthias Wagner asked an Apple engineer about managing the iPhone supply chain.“What software do you use?”“What software? Cubicles full of people. Each one has a phone and a list of numbers. They call suppliers all day, updating a shared spreadsheet. One person can edit at a time.”That’s the terrain. Not what the frameworks say. Not what the business models teach. The actual terrain.Summer 2019, Matthias left Facebook to build electronics in his Oakland workshop. Got frustrated immediately. The tools hadn’t evolved since the mid-nineties. No version control, no collaboration, no automation. Just paper processes ported to Windows.The map said hardware was hard because manufacturing was expensive and inaccessible.He tested it. Ordered any semiconductor in the world to his backyard. Unit quantity: one. Seven-day turnaround from China. A few hundred dollars.The supply chain had democratized completely. The design tools hadn’t budged.At Facebook, machine learning had transformed everything. Why not here?A friend told him, “Matthias, you’ve been complaining about this all summer. Do something about it.”That’s Flux.ai. Making hardware design as accessible as software development.But here’s the pattern: most builders read the map and execute. Matthias walks the terrain and observes.The anecdotes don’t match the data. The frameworks don’t capture the friction. The best practices miss the opportunities.He doesn’t trust what frameworks say should be true. He tests what is actually true. Sources information directly. Builds messy models. Notices the friction everyone accepts as baseline.The map said hardware required massive capital and factory connections. The terrain showed the real constraint was tools nobody had fixed in thirty years.That gap between map and terrain? That’s where opportunities hide.Most product strategy happens in conference rooms. You fill out canvases and positioning statements. Run the plays from the playbook. But you’re optimizing against a map, not reality.The reality is cubicles and phone calls managing materials for the iPhone. The reality is CAD software that looks identical to 1995. The reality is sourcing spreadsheets with single-user edit locks in 2025. These aren’t exceptions. This is how things actually work.You can’t spot that from the map. You have to walk the terrain. Get your hands dirty. Build something yourself. Ask the engineer doing the work how they actually do it.Test your assumptions with first-hand experience. That’s how you develop real product sense. That’s how you see opportunities others miss by trusting frameworks that describe a world that doesn’t exist.The map is where you start. The terrain is where you build.Listen on Apple Podcasts ↗ or Spotify ↗Guest: Matthias Wagner* LinkedIn: Matthias Wagner ↗* Company: Flux.ai ↗ Get full access to The Way of Product w/ Caden Damiano at www.wayofproduct.com/subscribe

Most teams aren’t doing the work to make a product launch successful. They’re pontificating in conference rooms. Debating specs. Trying to intellectually arrive at the right answer.The work looks different. The work looks different. Like Steve Jobs said, “There’s just a tremendous amount of craftsmanship in between a great idea and a great product… Designing a product is keeping 5,000 things in your brain and fitting them all together in new and different ways… And it’s that process that is the magic.”It looks like Dan De Mars and his team at Current Backyard are cooking 500 pizzas in two weeks. Prototyping. Testing with real data. Seeing if they can actually deliver before promising anything to the public.Crust thickness. Topping load. Heat curves. App guidance. Mouthfeel. Every variable they could isolate became another run, another data point, another step toward something that felt right—not just something that looked good in a deck.This is what intentional design actually looks like.Dan, head of product at Current Backyard ↗, doesn’t believe in perfection out of the gate. He believes in creating the conditions where a team can learn fast, fail often, and use their collective taste to sort signal from noise.The result? An electric pizza oven that lets a first-timer cook restaurant-quality pizza without the friction, the learning curve, or the open flame.But the insight goes beyond pizza ovens.Great products feel inevitable from the outside because teams did unreasonable amounts of work on the inside. They ran tight feedback loops. They invited more eyes. They treated taste as a filter over hundreds of experiments, not a single flash of genius.Dan also talks about designing for “limited grillers”—urban dwellers constrained by space, fire restrictions, or time—who still want great food without the heroics. It’s a masterclass in finding underserved segments and building for real constraints.If you’ve ever wondered how “it just works” products come together, this conversation is the blueprint.Listen on Apple Podcasts ↗ or Spotify ↗LinksDan De Mars on Linkedin ↗Current Backyard ↗00:00 The Essence of Design01:17 The Journey of a Designer02:17 Philosophy of Design03:25 Unlearning and Relearning09:44 Innovating Outdoor Cooking18:41 Targeting the Modern Cook25:56 Innovative Launches and Product Expansion26:52 Competing with Convenience: The Pizza Oven28:26 Designing for User Experience31:05 Prototyping and Iteration Process34:31 Balancing Functionality and Aesthetics44:29 Final Thoughts and Future Directions Get full access to The Way of Product w/ Caden Damiano at www.wayofproduct.com/subscribe

Enterprises do not have an AI problem; they have an AI governance problem.In my recent conversation with David Trier, VP of product at ModelOp, he described the current state inside large organizations as “the Wild West of AI”—dozens of teams, hundreds of tools, and no shared way to get models safely into production.The reality is that many enterprises are staring at portfolios of 50 to 100 generative AI use cases, but only a handful ever make it into production, often taking six to 18 months to ship.What clicked for me in this episode was David’s analogy: ModelOp is essentially ServiceNow for AI.ServiceNow gave IT leaders a consistent, auditable way to turn messy tickets into reliable service management. ModelOp does the same for AI initiatives: it sits at the enterprise layer, orchestrating 10 to 12 teams and systems—data, security, legal, risk, compliance, infrastructure—so AI projects move through a repeatable playbook instead of one-off review cycles.David walked through a financial-services case where this approach cut time-to-production in half, turning 18‑month science projects into AI services that ship in weeks and generate business value before models degrade.For product leaders and CTOs, the takeaway is simple: if AI is a C‑suite–sized investment, it needs C‑suite–grade governance, not grassroots experimentation scattered across the org.If you are thinking about how to move from proof‑of‑concept chaos to an enterprise AI operating model, this episode is worth your time.Listen on Apple Podcasts ↗ or Spotify ↗Mentions* ModelOp ↗* David Trier ↗ Get full access to The Way of Product w/ Caden Damiano at www.wayofproduct.com/subscribe

Brian Donohue, VP of Product at Intercom, shares his journey of integrating AI into product development, focusing on the creation of their AI agent, Finn. He discusses the evolution from early machine learning to leveraging large language models, reflecting on the significant challenges and innovations encountered. Brian emphasizes the importance of outcome-based pricing, aligning customer incentives with product success. He also explores the need for product teams to enhance their technical fluency to navigate AI complexities effectively.

In this episode, Vishal Khanna, Head of Product and Technical Go-To-Market at Exa.ai, shares insights on the ambitious mission to reinvent search in the AI-driven world. We talk about the challenges and opportunities in taking on industry giants, the importance of thoughtful application of AI tools, and the value of fundamental problem-solving skills. Vishal also discusses his transition from management consulting to product management, the necessity of technical literacy, and fostering high agency in teams. This conversation explores how integrating AI with sound decision-making can drive impactful innovation in the tech industry.Connect with Vishal01:14 Choosing to Work at Exa01:24 The AI Revolution and Its Impact02:25 Challenges and Opportunities in AI04:30 Managing Expectations with AI05:28 The Role of Product Managers in the AI Era11:23 The Importance of Technical Literacy22:25 Lessons from McKinsey25:52 Adapting to AI in Product Management33:04 The Concept of Agency39:31 Conclusion and Final Thoughts Get full access to The Way of Product w/ Caden Damiano at www.wayofproduct.com/subscribe

This one myth, in my opinion, is the leading cause of burnout: Unless you aim for the highest role at a company, you’ve somehow failed. My next guest on The Way of Product Podcast, Jamie Toyne, challenges that idea. Jamie’s been there and done that. he’s been the CEO, sold the company, and travelled the world. The right fit might be coaching, consulting, or serving as the creative force behind someone else’s vision—like Jony Ive was to Steve Jobs. Following formulas that ignore your true nature leads to burnout and misalignment—success is not measured by title, but by how invigorating the work feels. Enzo Ferrari, James Dyson, Dietrich Mateschitz (Redbull), Jony Ive, didn’t optimize for careers that make a bunch of money, they did work they unlocked their talents, gave them energy to be creative during the hard times, and the structure their creative expression. If you are doing great work that aligns with your unique talents and values, the market will reward you, companies will fight to retain you, and people will want to invest or purchase what you build because you are involved.Turns out, great work is hard to come by, and putting someone who’s energy isn’t aligned with the work doesn’t lead to great work. This episode is for anyone who doesn’t feel like they are doing work that aligns with their wiring. I certainly benefited from the conversation. -CadenListen to The Way of Product: Apple Podcasts or SpotifyIn this episode, Jamie Toyne recounts his personal and professional journey with ADHD—from growing up in Australia, building a successful mergers and acquisitions firm in San Francisco, to starting an ADHD coaching career. Jamie experienced significant burnout, moved to Mexico, and later sold his business.He discusses the challenges and benefits of living with ADHD as a business leader, the importance of doing work that aligns with personal values, and how his coaching program helps people with ADHD perform better and prevent burnout. Jamie explores recent changes in how society understands ADHD, and what these shifts mean for modern workplaces.LinkedIn: https://au.linkedin.com/in/jamietoyne1Free 1:1 ADHD Coaching Session: www.jamietoyne.com00:30 Journey to San Francisco—and burnout02:15 Life as a Digital Nomad03:48 ADHD Diagnosis and Early Life05:07 Tennis Career and Education08:44 Entrepreneurial Challenges and Burnout13:48 Balancing Ambition and Lifestyle18:46 Reflecting on Business Structure and Superpowers19:11 Dealing with Imposter Syndrome and Financial Decisions20:35 Team Dynamics and Company Culture22:50 Passion for Coaching and Exit Planning26:50 ADHD and Leadership Challenges33:14 Finding Flow and Realigning with Passion Get full access to The Way of Product w/ Caden Damiano at www.wayofproduct.com/subscribe

What happens when your biggest customer asks for a feature that seems perfectly rational—backed by data, supported by sales, and tied to six-figure deals? Kritarth Saurabh shares how Neat avoided the build trap by pausing to validate what customers actually needed versus what they requested. This conversation explores the difference between output-driven and outcome-driven roadmaps, and why the hardest word in product management isn’t “no”—it’s “wait.”Key Topics Discussed:* The Mural integration trap: How responding to customer feature requests can lead to becoming an integration factory* Output vs. outcome-driven roadmaps: Why shipping features fast matters less than scaling the right thing* The validation framework: Moving from idea to experiment to validated roadmap before building* Qualitative vs. quantitative data: When to trust customer anecdotes over usage metrics* Zero-to-one product development: Building without data in early-stage companies* Meeting equity and hybrid work: How Neat approaches designing for distributed teams* Simplicity in hardware: The phone camera principle and why accessibility beats perfectionKey Quotes:“You gotta have the conviction to take a step back and say, look, what is the real outcome that I’m trying to drive here?”“If I had just spent maybe the next quarter validating this as an experiment...what they would’ve told me is they want App X, they want Figma, they want Y...This is not about just making the dollar signs with the mural. This is about the wider customer problem.”“The hardest word in product isn’t ‘no’—it’s ‘wait.’”“Often moving slow is a problem...but I think a bigger problem is not scaling the right thing.”Featured Story:The Mural Integration Decision: Kritarth details how a seemingly rational request for a Mural integration—backed by top-three usage data and tied to major deals—would have led Neat down the path of building an integration team that services infinite requests. By spending a quarter validating the underlying customer need, they discovered enterprises wanted workflow integration across their entire app ecosystem. This insight led to building an App Hub marketplace instead, creating a platform that scales exponentially rather than linearly.Resources Mentioned:* Book: Escaping the Build Trap by Melissa Perri* Neat’s App Hub marketplace* Product Kata frameworkAbout the Guest:Kritarth Saurabh is VP of Product Management at Neat, a video conferencing hardware company focused on simplicity and meeting equity. Before Neat, he spent years in consulting at Boston Consulting Group, Accenture, and Deloitte, working with Fortune 500 companies and startups on product development. He started his career as a software engineer and has experienced the full product lifecycle from ideation to sunsetting.Connect with Kritarth: https://www.linkedin.com/in/kritarthsaurabh/About NeatNeat manufactures video conferencing devices designed to keep meetings simple and equitable, whether participants are in-office, remote, or hybrid. Their products include the Neat Board Pro, an all-in-one 65-inch integrated screen with camera and speaker capabilities.Subscribe:www.wayofproduct.com Get full access to The Way of Product w/ Caden Damiano at www.wayofproduct.com/subscribe

I used to say, “Don’t let AI do your research.” I’ve changed my mind.That shift started before this interview—after I ran a complex API exploration through an AI research assistant and got back a thorough, sourced report with working links. But my conversation with Margaret-Ann Seger (who leads product and design at Statsig, the intelligence infrastructure platform for feature flags, experimentation, and analytics) turned that insight into conviction. And the timing makes this even more interesting: Statsig recently announced it’s being aquired by OpenAI. If OpenAI sees enough value to combine forces, the way Margaret and her team work is worth studying.Here’s the core change I’m making in my own work: let AI collapse the paperwork so humans can concentrate on judgment. When I dump a messy outline into a model and it returns a clean structure in minutes, I don’t feel threatened; I feel focused. Margaret described a world where PRDs update themselves from meeting inputs and auto-ticket the next steps. That’s not cutting corners. It’s cutting ceremony. The value we bring isn’t keystrokes—it’s synthesis.Synthesis shows up in how we decide what to build when shipping gets cheap. AI lowers the barrier to creation, which raises the bar on taste. It’s not enough to ship more; you have to choose better—distill the real pain, reconcile what users say with what they actually do, and shape solutions that feel right in the hand. Margaret triggered a new habit for me: I now write a one-paragraph “taste test” before we commit: Why this problem? Why now? Why this approach? If I can’t explain it plainly, we aren’t ready.The conversation also reframed “soft skills” as the durable edge. You can’t paste three years of team history into a prompt. Reading the room, sensing when engineers don’t buy a solution, remembering why a past decision failed—these are still human advantages. Margaret called out the friction every PM knows: users tell you one thing in interviews and do the opposite in product. Someone has to hold both truths at once and decide. That someone is still us.One practice of hers made that human edge tangible: make customer support everyone’s job. At Statsig, support isn’t a silo. They rotate it. Designers answer confused tickets and see where the UI collapses. Engineers feel the frustration firsthand and often fix root causes quickly. It’s tempting to route everything through a bot for speed, but there’s a hidden cost: you lose the raw empathy that powers taste. We’re piloting a similar rotation and tracking the fixes it sparks.Another theme was moving learning into production. Prototypes were born when shipping was expensive. As that cost falls, high-fidelity demos give way to small, live experiments that gather real data. Margaret’s ideal cadence is to spend more time on problem analysis and then release multiple small bets behind flags. I’ve started doing the same for ambiguous flows: define two or three minimal viable variants, ship them to real segments, and time-box the learning window. Data beats debate.On the tooling side, Margaret pushed me to point AI at sources of truth, not just the documentation. Docs always lag. Code doesn’t. Her team is exploring agents that answer questions grounded in the codebase and SDKs. I loved the example of customers repurposing Statsig’s experimentation tool to benchmark models and prompts offline—a reminder that good tools get bent into new jobs in the AI era. We’re trialing a code-aware path for technical support and an internal agent trained on our repos for integration questions.Something else I’m now normalizing: don’t hide your AI usage. Margaret hired a PM who clearly used AI on the take-home. That wasn’t a disqualifier; the deciding factor was his judgment. The stigma needs to go. Show your work, raise the standard, and trade playbooks. We’re adding a simple line to retros: “How did AI help?” When the practice is visible, everyone gets better faster.Two moments from the interview keep replaying for me. The first was our “soft skills” segment, because it names what PMs actually do when the tools get powerful: we arbitrate between truths, people, and paths. The second was personal and small—Margaret and her husband use AI to make songs for everyday moments and stories for their kid. It’s a reminder that this wave isn’t only about efficiency; it can unlock more human connection at scale.Here’s where I’ve landed:* I no longer treat AI as a novelty or a threat. I treat it as an accelerant. It compresses the “what” so I can deepen the “why.”* I’m biasing toward live learning and away from document theater: fewer perfect specs, more real outcomes.* I’m putting empathy on the front line (support rotations), taste at the gate (the one-paragraph test), and code at the center of truth (repo-grounded agents).If you’ve been skeptical like I was, start small: choose one active project, let AI handle the formatting and ticketing, and spend the saved hour with a customer or sharpening the problem statement. You may find your job doesn’t get smaller. It gets truer.And in a world where a company like Statsig is merging with OpenAI, getting to that truer version of product work isn’t optional—it’s the edge.Listen to The Way of Product: Apple Podcasts or SpotifyMargaret-Ann Seger, leader of product and design teams at Statsig, and after the acquisition from OpenAI, part of their Product Staff, discusses the evolving role of AI in product management on the latest episode of The Way of Product. While AI can automate many tasks, human judgment remains crucial. She shares insights on how AI can supercharge productivity and reduce drudgery, allowing PMs to focus more on strategic thinking and deeper customer understanding. Margaret also explores the idea that future PM tasks will blend with design and engineering roles, facilitated by AI tools. She remains optimistic about AI's impact on creativity and productivity.Listen to The Way of Product: Apple Podcasts or Spotify00:00 Introduction to AI and Human Judgment00:33 Meet Margaret Ann from Statsig01:16 Debating the Future of Jobs in Tech02:45 The Importance of Taste in Product Management06:34 Soft Skills and Human Empathy in Tech12:33 The Role of Engineering Background in Product Leadership15:46 AI's Impact on Research and Data Gathering20:23 Embracing Technological Progress20:42 The Joy of Creating with AI22:18 AI in Product Management24:32 The Future of Work with AI25:45 Exploring AI Tools and Their Impact31:23 The Role of AI in Knowledge Management34:21 Optimism for the Future35:25 Closing Thoughts and EncouragementThank you for reading this week's episode of The Way Product. This publication is intended to be free indefinitely. Get full access to The Way of Product w/ Caden Damiano at www.wayofproduct.com/subscribe

Declining MAU can be a product win,depending on your product strategy...Andrew Saxe, VP of Product at Smartling, joined me to unpack why.When a system translates intent well and runs in the background, people don’t have to log in—and the work still gets done.We discussed designing for “manage by exception,” placing guardrails where failure would be costly, and allowing automation to carry the load.We looked at the feature request funnel too: why thousands of asks don’t equal thousands of tickets, and how building a few verbatim specs for large customers can still lead to unused features, tech debt, and wasted opportunities.Listen to The Way of Product: Apple Podcasts or SpotifyTimestamps:02:48 The niche translation industry and Smartling’s impact11:28 The role of AI in translation13:55 AI’s impact on Smartling’s product roadmap16:38 Managing translations with AI and human oversight22:00 Optimizing translation services for market expansion22:47 Challenges and nuances of multilingual customer research23:41 The art and science of requirements to spec translation29:18 Feature requests and customer feedback31:26 The importance of understanding user needs34:27 The future of translation and AI38:37 Concluding thoughts and contact information24:56 AI and Translation: Balancing Automation and Human Touch28:19 Feature Requests and Customer Feedback30:27 The Importance of Understanding User Needs35:42 Managing Translation Budgets and Quality37:39 Conclusion and Final Thoughts Get full access to The Way of Product w/ Caden Damiano at www.wayofproduct.com/subscribe