The Growth Podcast

Gabor Meyer, a Google product manager who builds AI-powered mobile apps with multi-agent Claude Code, shares his approach. He explains why treating Claude Code as a team beats single-prompt prototypes. He outlines a 21-agent architecture, a spec-first workflow, automated design-to-code pipelines, and practical steps for shipping apps from idea to TestFlight.

Mahesh Yadav, experienced AI product leader and founder who built AI products at Microsoft, Amazon, Meta, and Google. He explains what being a builder PM means and breaks down agent design into model, tools, memory, and knowledge. He demos n8n for prototyping, when to move to Claude Code, and the OpenClaw pattern for safe delegation and sandboxed workflows.

Ed Bayes, design staff at OpenAI who codes heavily, and Gui Seiz, Director of Product Design for AI at Figma who ships design PRs. They debate code versus canvas and when each wins. They demo importing running React screens into Figma and discuss design tokens, reliability of AI tools, and how teams enable designers to ship production code.

Hannah Stulberg, a DoorDash product manager and former Google APM who created the 'Claude Code for Everything' series, shares her Team OS approach. She explains a shared repo that scales PM context, the lean root CLAUDE.md and nested indexes, and how to structure docs, agents, and metrics so everyone self-servers. Practical tips on plan mode, verification phases, and saving prompts round out the conversation.

Carl Vellotti, AI/product management practitioner and educator specializing in Claude Code, shares how to stop using the tool like a chatbot and instead build skills, subagents, and an OS around context. He covers context management, subagents that preserve context, auto-triggering skills with hooks, and trustworthy Jupyter-backed data analysis. Short, practical, and focused on workflow architecture.

Jyothi Nookula, an AI product leader who built AI teams at Meta, Amazon and Netflix and trains PMs into AI roles. She breaks down AI PM types and the three-layer AI PM stack. Short takes cover when to use AI versus heuristics, agent vs workflow design, RAG basics, and portfolio tips for breaking into AI product work.

Ankur Goyal, founder and CEO of Braintrust, leads a major eval platform used by top AI product teams. He explains why evals should start the product process, how to build data-task-score evals, and shows a live demo creating an eval from scratch. Conversation covers scoring design, connecting evals to tools like Linear, and using failing evals as a roadmap for improvement.

Naman Pandey, an AI/product tooling practitioner who creates hands-on agent tooling tutorials. He walks through OpenClaw setup with three terminal commands and common pitfalls. He demos Slack integration, cron‑based automations like standups and competitive intel, model and gateway choices, security checks, and deployments for persistent, autonomous agents.

Dave Killeen, Field CPO at Pendo with ~25 years in product and creator of DEX, walks through a personal OS built in Claude Code. He demos a one-command morning routine, MCP integrations, and automated PRD and Kanban workflows. Hear how he combines GitHub scans, newsletter and YouTube intelligence, career readiness MCPs, and session hooks to run an entire work life from a terminal.

Today’s episodeMost PMs are using AI the same way they used Google in 2005.Type something in. Get something out. Move on.That is not how the best PMs are using it. The best PMs have stopped treating AI as a search engine and started treating it as a team member. One that already knows their product, their writing style, their strategy. One that does not need to be briefed from scratch every single time.That shift is what today’s episode is about.I sat down with Lisa Huang, SVP of Product at Xero, an $18 billion finance platform. She built the AI assistant for the first generation Meta RayBan smart glasses. She created Gemini Gems at Google. She has been an AI PM at Apple, Meta, and Google - three of the most demanding AI product environments in the world.She gave us a masterclass across Gemini Gems, building AI into hardware, running AI agents at scale inside a financial product, and what the AI PM career actually looks like from here.In today’s episode, we discuss across three topics.* How to build Gemini Gems and AI projects that actually work.* What she learned building AI into a wearable device.* What the future of the AI PM career actually looks like.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by - Reforge:Get 1 month free of Reforge Build (the AI prototyping tool built for PMs) with code BUILD----If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.----Key takeaways:1. Stop briefing your LLM from scratch every time - Gemini Gems hold your context permanently. Your role, your company strategy, your writing style. Build it once and it already knows everything the next time you open it.2. Every PM needs 3 Gems - A writing clone trained on your PRDs and emails. A product strategy advisor loaded with your company docs and competitor analysis. A user research synthesizer that ingests raw transcripts and surfaces key themes.3. Vague instructions are the number one mistake - "Help me write better" gets you nothing. Write a full page of context. Your role, your audience, your format preferences. The more specific, the more personalized the output.4. Gemini Gems vs ChatGPT custom GPTs - OpenAI framed GPTs as an app store ecosystem. Google focused on personal productivity instead. First principles beat copying a competitor's framing, and the GPT store never took off.5. On-device AI is the future for wearables - Cloud is the default today but once a device is on your face all day, people want their data staying local. Privacy beats performance when the device is that personal.6. Accuracy is the product in high-stakes AI - LLMs out of the box are not great at math, accounting, or tax. Winning agents combine deep domain knowledge with proprietary data that no general-purpose model can access.7. Measure agents in three layers - Quality first (evals, human annotators, LLM judges). Product metrics second (adoption, retention, CSAT). Business impact third (revenue attribution, ARR). Skip to layer three without the foundation and you are measuring on sand.8. AI will not replace PMs - it will replace the execution work. Writing PRDs, creating mocks, managing roadmaps. What stays is product judgment. The ability to look at ambiguous signals and say this is the right bet and here is why.9. The PM role is becoming a hybrid - PM to engineer ratios will compress. The expectation is that PMs also build. Not just spec and hand off, but prototype, design, and code enough to show what they mean. The tools to do this exist right now.10. Your company's permission is not required - Most companies are not fine-tuning models. They are using the same consumer tools you already have. Build Gems. Build projects. Build small AI products with your personal data. There is nothing stopping you.----Where to find Lisa Huang* LinkedIn* WebsiteRelated contentNewsletters* How to become an AI PM* Practical AI agents for PMs* AI evals explained simply* AI product strategy* The AI PM learning roadmapPodcasts* Claude Code + Analytics - Vibe PMing with Frank Lee* AI evals explained simply with Ankit Shukla* How to become an AI PM with Marily Nika* AI prototyping mastery with Sachin Rekhi----PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!If you want to advertise, email productgrowthppp at gmail. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribe