Elena Verna: Why PMs who can't build will get left behind

Crystal Widjaja: Why PMs Must Get Comfortable with the CLI

Crystal Widjaja, former Chief Product Officer at Kumu and a Reforge instructor, has built data and growth systems at huge scale. Most product managers still use AI for quick summaries and one-shot artifacts. Crystal has spent her "semi-retirement" building something more ambitious: a system of AI agents that handles everything from her daily prep to a "friendship CRM." In this episode, she walks through the messy reality of her stack, built with Obsidian, Python, and Claude Code, and shows how PMs can get past the trap of polished GUI tools and start building infrastructure that scales their impact. What we discuss • Moving from surface-level prompting to mature agent infrastructure and "context engineering" • Automating low-leverage work like meeting notes and status updates, so you can spend time on strategy and customers • The AI advantage in Southeast Asia, where a culture of delegation has prepared leaders for working with agents • Three things that won't change: vision setting, creative voice, and deep customer empathy • The "Ralph Loop": chaining complex AI tasks together for autonomous execution Key takeaways PMs need to get comfortable with the CLI. Crystal argues that sticking to simplified AI interfaces stops you from understanding the system underneath. To improve your workflow and use AI fully, you have to be willing to open the terminal and work with the technical scaffolding yourself. Context is the new prompt engineering. The first wave of AI was about learning how to prompt. The next wave is about context engineering. Output quality depends on what you feed the system, from meeting transcripts to project files, and on building pipelines that sync that context automatically. Start with the smallest unit of work. Instead of aiming for a big-bang AI solution, automate one specific task you hate, like syncing meeting notes to your computer. Once that works, iterate and chain those units into a fuller agent system. AI should make you more human, not just more productive. Crystal uses her AI chief of staff to run a "friendship CRM," tracking things like a friend's offhand mention of a marathon or a gift idea. Offloading the menial memory work frees up energy to be a more present, thoughtful friend. Adopt a "semi-unemployed" mindset. The best way to stay relevant is to leave enough room in your schedule to play with new tools. Automating low-leverage tasks buys you the time to stay ahead and learn how agent orchestration is changing. Chapters • 00:00 - Introduction to Crystal Widjaja • 02:08 - AI adoption and practice in Southeast Asia • 04:24 - Demystifying AI through practical workflows • 07:41 - AI maturity: engineering vs. product management • 11:11 - Practical steps to build AI fluency • 14:34 - Critical skills for the AI era • 18:48 - From prompt engineering to context engineering • 22:13 - Timeless product management fundamentals • 23:59 - Demo: building an AI "chief of staff" • 27:59 - Automating work with "Ralph Loops" • 36:38 - Impact on personal productivity and relationships • 39:46 - Hype, reality, and the future of AI in 2029 • 41:51 - Final habits for effective AI use About Atlassian Behind every great human achievement, there is a team. From medicine and space travel to disaster response and pizza deliveries, we help teams all over the planet advance humanity through the power of software. Our mission is to help unleash the potential of every team.

Elena Verna: Why PMs who can't build will get left behind

Elena Verna is Head of Growth at Lovable, one of the fastest-growing AI-native companies. She's previously held growth and product leadership roles at Dropbox, Miro, Amplitude, and SurveyMonkey, where she built some of the most influential frameworks in product-led growth. We discuss * The shift from "product manager" to "product builder" and why interviews now require working prototypes * Automating Growth 101 work so you can focus on creative strategy * The collapse of the talent stack: how roles in product, design, and engineering are blurring * Three things that won't change: customer interaction, vision setting, and understanding distribution * The "superpower framework" for career-accelerating decisions Key takeaways * PMs who can't build are becoming obsolete. The shift isn't theoretical or five years away. Top tech companies are already changing their interview processes to require functional prototypes instead of case studies. If you can't translate your product thinking into something tangible, you're competing with one hand behind your back. * Automate Growth 101, then invest the freed-up time in creative strategy. The repetitive mechanics of growth marketing (A/B tests, funnel analysis, campaign setup) are increasingly automatable with AI. Elena argues that the PMs who thrive will be the ones redirecting that time toward the harder, more creative work that machines can't do yet. * The talent stack is collapsing, and that's exciting. Boundaries between product, design, and engineering are blurring as AI gives individuals more autonomy across disciplines. Elena sees this as liberating rather than threatening. The PMs who embrace building, not just specifying, can move faster and validate ideas without waiting for a team to assemble. * Three foundations won't change, no matter how good AI gets. Customer interaction, vision setting, and understanding distribution remain irreplaceably human. AI can accelerate execution, but it can't replace the judgment that comes from sitting with customers, setting a compelling direction, or knowing how your product actually reaches people. * Your superpower lives at the intersection of what you're good at and what you love. Elena's career accelerated when she stopped trying to be well-rounded and started leaning into product-led growth as her defining skill. Find that intersection, then make career moves that compound on it rather than scattering your energy across unrelated opportunities. Chapters 00:00: Introduction and highlights 01:13: Welcome and the current state of tech 02:10: Why Elena joined Lovable and automating Growth 101 05:36: The shift from product managers to product builders 08:39: Advising growth-stage companies on AI adoption 11:56: Fostering AI fluency and bottom-up adoption 14:54: Raising AI fluency at scale beyond checkbox exercises 17:38: The collapse of the talent stack: product, design, and engineering blur 20:42: Critical skills for the AI era PM 24:16: Three things that won't change: customers, vision, and distribution 28:20: Demo: Building a nonprofit offering spec and prototype with ChatGPT and Lovable 47:01: Lightning round: podcasts and Substacks 49:22: Career-accelerating decisions and the superpower framework 52:00: Desert island favourites and closing #productmanagement #PM #atlassian About Atlassian: Behind every great human achievement, there is a team. From medicine and space travel to disaster response and pizza deliveries, we help teams all over the planet advance humanity through the power of software. Our mission is to help unleash the potential of every team.

Cemre Güngor: How to ship AI features users actually use | Product in Practice

Cemre Güngor has helped shape how billions interact with technology, from Facebook and Instagram's global sharing experiences to Figma's AI-powered workflows, and now reimagining the browser at The Browser Company with Dia.In this conversation, we break down: * Product Craft in the AI Era * Why starting with the end in mind matters more than ever when prototyping is democratized * The difference between durable AI features and "demo candy" * How to evaluate prototypes without clear problem definition * Scaling Products Across Surfaces * What changes (and what doesn't) when building for social platforms vs creative tools vs AI-native workflows * Why product managers get too attached to their existing mental models * The hidden costs of ignoring new interaction paradigms * Real AI Workflows at The Browser Company * Practical examples: automated communication audits, personality type predictions, and workflow optimization * Why meeting transcripts are the most underutilized data source in remote work Jira Product Discovery is the home for all your product ideas, customer feedback, and bets, so you can prioritize what actually matters and share living roadmaps with your stakeholders. Turn scattered requests and spreadsheets into a single, shared source of truth that connects directly to delivery in Jira. Get it free at https://www.atlassian.com/software/jira/product-discoveryChapters01:31 Welcome back to Product in Practice03:21 Starting with the end in mind and focusing on problems05:36 Product management behavior that stopped working, moving from Instagram to Figma08:30 Shift from metric-driven to judgment-based product management09:18 Cemre's background with AI at Figma10:50 Learning by doing and shipping quickly14:39 How LLM-based features change product development16:59 The Browser Company's focus on design and intentionality19:10 The single most important test for an AI feature: internal usage22:20 How evolving AI models (reasoning models) impact product roadmaps23:03 The Bitter Lesson: When to wait for model improvements25:09 Why agentic workflows (like computer use models) are not a priority yet26:12 The necessity of shipping first to learn what good looks like for general-purpose AI assistants29:22 The browser's responsibility in an AI world: holding all your context32:47 The key value of an AI browser: bringing in your context34:33 Advice for PMs: Do the boring thing with AI37:06 Demo: Simulating your manager's perspective (the "Josh skill")42:18 Demo: Revising a document with real meeting feedback/transcript47:00 Demo: Daily priority recommendations based on all context (Memory, Calendar, Slack)51:09 How AI tools have transformed productivity and PM workflows53:54 Demo: Coaching and feedback on communication style from all channels54:41 The advantage of browser-level integrations like Dia'sReferencedArc Search (mobile app): https://arc.net/search Arc (browser): https://arc.net/ Gemini (model): https://gemini.google.com and https://ai.google.dev/gemini-api/docs... Cursor: https://cursor.com/ Claude Code: https://claude.com/product/claude-code GPT Codex: https://openai.com/codex/ Granola (note-taking tool mentioned): https://www.granola.ai/ Four Thousand Weeks:  https://www.goodreads.com/book/show/54785515-four-thousand-weeksAbout Atlassian:Behind every great human achievement, there is a team. From medicine and space travel to disaster response and pizza deliveries, we help teams all over the planet advance humanity through the power of software. Our mission is to help unleash the potential of every team.

Aakash Gupta: The skill every product manager must learn in 2026

What if you could build product prototypes in 15 minutes instead of 3 weeks?In this episode, Aakash Gupta (creator of the Product Growth Newsletter and former VP of Product at Affirm, Epic Games, and ThredUp) reveals the AI prototyping workflow that's revolutionizing product management.Watch as he live-demos building a sleep tracker app using Bolt, V0, and Lovable, showing you exactly how to go from concept to a working prototype in minutes.What You'll Learn: * The 6-step framework every PM needs to master AI product building * How to use Bolt, V0, Lovable & Cursor for rapid prototyping * Why AI prototyping is the most significant change product management has ever seen * Real-time demo: Building a sleep score calculator from scratch * The exact prompt engineering techniques that get better results * How to iterate across multiple AI tools to find the best output * Start with problem discovery (not jumping straight to solutions) * Test across multiple tools (Bolt, V0, Lovable) * Iterate with detailed feedback * Move to Cursor for final refinements The Four-Step AI Prototyping Method:"What used to take weeks now takes literally minutes." This isn't hype; this is the new reality for product builders.About Aakash Gupta: Long-time product leader at ThredUp, Epic Games, Affirm, and Apollo. He now runs the Product Growth Newsletter (Substack), featuring practical playbooks and AI experiments for product builders.Where to find Aakash Gupta: LinkedIn: https://www.linkedin.com/in/aagupta/ [https://www.linkedin.com/in/aagupta/] Where to find Axel: Linktree: https://linktr.ee/axelsooriah [https://linktr.ee/axelsooriah] LinkedIn: https://www.linkedin.com/in/axelsooriah/ [https://www.linkedin.com/in/axelsooriah/] About Atlassian: Behind every great human achievement, there is a team. From medicine and space travel to disaster response and pizza deliveries, we help teams all over the planet advance humanity through the power of software. Our mission is to help unleash the potential of every team. Connect with Atlassian: Subscribe: https://www.youtube.com/channel/UCL1yMVRMh3vxitPiVaXfkoA [https://www.youtube.com/channel/UCL1yMVRMh3vxitPiVaXfkoA] Follow Atlassian on LinkedIn: https://www.linkedin.com/company/atlassian [https://www.linkedin.com/company/atlassian] Follow Atlassian on Instagram: https://www.instagram.com/atlassian/ [https://www.instagram.com/atlassian/] Follow Atlassian on X: https://x.com/atlassian [https://x.com/atlassian] Follow Atlassian on Facebook: https://www.facebook.com/Atlassian/ [https://www.facebook.com/Atlassian/] If you're a PM, designer, or engineer building AI products, this is required viewing for 2025.

Kene Anoliefo: How to build context libraries and raise AI fluency

Kene Anoliefo has led product and design at Google, Spotify, and Netflix. She founded Heard, an AI-powered user research platform, and is now building tools that help teams capture and structure institutional knowledge so both people and AI can produce high-quality work. We discuss * The three Ps of AI adoption: people, process, and platformWhy most companies jump to platform tools whilst ignoring the people problem * Moving from gatekeepers to architects in product organisations * Context enablement: turning implicit knowledge into explicit AI inputs * The coordination tax and how AI changes team collaboration * Why AI is like a brilliant but clueless new team memberBuilding context libraries to 10x your AI output quality Key takeaways Start with people, not platform tools. Most companies rush to buy AI tools whilst ignoring the deeper questions: Who am I in this AI era? What makes me valuable when AI can do parts of my job? Anxiety about AI is rooted in identity, not technology. Address the people layer first before investing in new platforms. Transform from gatekeepers to architects. Don't own user research, product strategy, or design - architect the principles and standards so anyone can do great work in those domains. With AI giving everyone superpowers, your value shifts from creating scarcity to enabling abundance. Context is your competitive advantage, not just data. AI trained on generic internet data produces generic output. Context libraries - structured repositories of your product strategy, design principles, customer segments, and research standards - help AI understand how your company builds products. AI is brilliant but clueless without your context. Think of AI as a talented new hire who needs onboarding. It has capabilities but no understanding of your product, customers, or how your team works. Just like you'd explain these things to a new employee, you need to provide this context to AI tools through documentation optimised for machine reading. Don't wait for permission to engage with AI. The biggest barrier isn't technical knowledge - it's mindset. You don't need to have worked at ML-first companies or taken AI courses. The cure for AI anxiety isn't trying to learn everything; it's starting with work you already love and asking how AI can make it 10-20% better. Chapters00:00: Introduction to Kene Anoliefo01:00: Kene's journey from Spotify and Netflix to founding Heard04:00: Early interest in AI and building AI-powered research tools07:00: Overcoming AI anxiety and intimidation10:00: The three Ps framework: people, process, and platform14:00: Why feelings matter more than tools in AI adoption15:00: The coordination tax and new team dynamics18:00: Gatekeeping vs enabling in the AI era20:00: Context enablement and knowledge architecture23:00: AI as a brilliant but clueless team member26:00: Career advice: take more shots on goal28:00: Making context enablement practical and actionable30:00: Demo: Building context libraries for AI33:00: Creating user research context libraries36:00: Using AI to generate context documentation39:00: Before and after: brainstorming without context42:00: The multiplier effect of context investment45:00: Context engineering as the next frontier48:00: Design context libraries and component repos51:00: Prototyping with V0 using pre-built contexts56:00: Democratising product ideas across the organisation59:00: Lightning round: staying sharp in the AI era01:01:00: Desert island books and dishes01:04:00: Final advice and where to find Kene Where to find Kene Anoliefo:LinkedIn: https://www.linkedin.com/in/kene-anoliefo-68a8b714/Website: https://kene.ai [https://kene.ai] Where to find Axel:Linktree: https://linktr.ee/axelsooriah [https://linktr.ee/axelsooriah]LinkedIn: https://www.linkedin.com/in/axelsooriah/ #productmanagement #PM #atlassian About Atlassian:Behind every great human achievement, there is a team. We help teams advance humanity through the power of software.