Stop Filling the Leaky Bucket: Why Retention Comes Before Acquisition | Jaclyn Zhuang

Stop Filling the Leaky Bucket: Why Retention Comes Before Acquisition | Jaclyn Zhuang

Jaclyn Zhuang [https://www.linkedin.com/in/jaclynzhuang] grew up in Singapore, spent years consulting across Southeast Asia, and came to American tech with what she calls an outsider's view. Her path runs through Deloitte, EA, Google, Twitch, Facebook, Zoom, Atlassian, and now as a former VP of Product and GM for Eightfold AI. In this episode, we cover: * How gaming at Electronic Arts taught her systems thinking - and why multiplayer game launches are one of the most demanding product environments you can work in * Why she made every career move without a grand strategy, and the principle behind each one: you don't need a plan, you need a good reason * The leaky bucket moment - leadership pushing hard on acquisition while retention was quietly broken, and why fixing the foundation first was the right call, even when it wasn't the exciting one * Her metric for evaluating any business: seats used divided by seats sold, and what it reveals before the churn shows up * Why structuring PM teams around features is a mistake, and what changes when you organize around personas instead * The AI hesitation she sees outside Silicon Valley - policy vacuums, EU AI Act, workers' councils - and why the real opportunity is closing the adoption gap, not building better models * Two things she's carried throughout her career: aim for the best given the circumstances, not perfect, and logic only moves people so far A conversation for PMs and product leaders who want to think more clearly about where to actually focus, from retention and org design to what enterprise AI adoption really looks like on the ground.

Data Is the Moat: What Working with the Frontier Labs Actually Teaches You | Deepak Tiwari

Deepak Tiwari [https://www.linkedin.com/in/detiwari] came up through engineering and strategy consulting at Accenture before moving into product, and his career has run through some of the most consequential AI and infrastructure builds of the last 15 years, across Google, Lyft, Turing, and Meta. At Turing, he served as CPO and built the training data platform used by OpenAI, Anthropic, DeepMind, and Meta. Before that, he helped take Google Cloud from fewer than $0 to $500M in annual revenue. He's now at Meta, leading product across ranking, relevance, and generative AI systems at Instagram scale. In this episode, we cover: * What it took to pitch Google Cloud's CEO on a net-new product from scratch, and what happened when customers started using it in ways the team never anticipated * How the enterprise transformation playbook that worked at Google Cloud became the blueprint for Turing's 10x growth * What working directly with the frontier labs taught him about why data is the real moat in AI * Building Meta's first generative AI ads product with no prior playbook, and reading transformer and diffusion model papers from scratch to understand what was actually possible * Why the biggest drivers of user experience at Lyft had nothing to do with the app, and everything to do with backend ML * The three levers that actually improve a model, and why good PMs need to hold all three in view * Why the manager who barely had time for one-on-ones taught Deepak the most important career lesson An interesting conversation for PMs and product leaders who want to understand how AI products actually get built, from the infrastructure up.

Build vs Buy, Acquisition Integration, and Launching Agentic AI in Construction | Rajitha Chaparala

Rajitha Chaparala [https://www.linkedin.com/in/rajithachaparala/] has spent 20 years building data and AI products in some of the world's most heavily regulated industries. She founded Intralinks' AI Center of Excellence, scaled the data platform at ZoomInfo through two simultaneous acquisitions delivering eight figures of incremental revenue in year one, and has spent the last four years as VP of Product for Data and AI at Procore, bringing AI into one of the most document-heavy, safety-critical industries in the world. In this episode, we cover: * How she built AI for M&A due diligence when she couldn't access actual customer documents, and why crowdsourcing training data on Upwork was the only path forward * The redaction product that emerged as a happy accident from the same NLP foundation, and what that taught her about building on existing platforms * The five principles she used to run two massive parallel workstreams during ZoomInfo's acquisition integration, and why clarity of vision matters more than process when you're moving fast * The build vs buy decision at every company she's been at, and why the answer has been different every time * Why human-in-the-loop isn't a limitation in construction AI, it's the right design for where accuracy and governance currently stand * Why she hires for adaptability above everything else, and what breaks when you don't A conversation for data and AI product leaders who want a grounded view of what it takes to build AI products in industries that don't forgive mistakes.

Building Across Every Platform Shift - Google, Oculus VR and Beyond | Robert Hamilton

Firstly, welcome to Season 3, and thank you to all my listeners! Please follow and rate the show so I can keep bringing on incredible Product leaders on the podcast! Robert Hamilton [https://www.linkedin.com/in/roberthamiltoncoach/] has been building products since before the Web existed. He founded the world's first SMS search and shopping company in 1999, spent 8 years at Google shipping the Google Mobile App, Voice Search, and Nexus hardware, and helped build the Oculus VR platform before anyone had figured out headset retention. He now helps and coaches PMs and product leaders full-time to accelerate their careers. In this episode, we cover: * What it was like building Scan Mobile in 1999, raising $10M, and learning that being early pays the same as being wrong * How Google approached app distribution before app stores existed, and what that bet taught the team about building for the future * The real challenge of VR retention and why getting someone to put something on their head is harder than it sounds * What keeps pulling Robert toward the frontier rather than the established thing * Why the biggest barrier holding PMs back has nothing to do with skills or AI tools * His framework for PMs at every stage: increase clarity and drive progress A conversation for PMs who want to think differently about building, navigating platform shifts, and understanding themselves before they try to understand the technology. You can find out more about Robert's current work here [https://www.accelerationcoach.com/].

Inside AWS, Stripe & Google Cloud: The Hard Truth About Scaling Enterprise Platforms | Bob Krentler

How do you actually scale enterprise platforms inside companies like AWS, Google Cloud, and Stripe? In this episode of Zero to One, I sit down with Bob Krentler [https://www.linkedin.com/in/bobpm/?miniProfileUrn=urn%3Ali%3Afs_miniProfile%3AACoAAALjq2AB-bg-_ZciitoOdgL51SkZ3HuO9Zw], former GM at AWS and Product leader at Stripe and Google Cloud, to break down what it really takes to build, operate, and monetize Enterprise-grade platforms at massive scale. Bob has led multi-billion dollar product lines, driven Enterprise modernization efforts across the cloud, and built vertical solutions powering some of the world’s most important companies. We cover: * The biggest mistakes teams make when modernizing legacy infrastructure * What AWS taught him about operating at true cloud scale * The hardest trade-offs in enterprise, including competing with your own partners * Why every horizontal platform eventually moves vertical, and how Stripe executed it * What most product leaders get wrong about AI-first product strategy * How AI is reshaping the role of product, engineering, and decision-making * Why enterprise requires long-term thinking, not short-term wins If you’re building for Enterprise, thinking about Platforms, or trying to understand where AI fits into your roadmap, this episode is a masterclass in how the best operators actually think.