Product Growth Podcast

• Full Roadmap: Become an AI PM in 2025 | Ankit Shukla, HelloPM Founder

  If this 80 minutes podcast doesn't make you an AI PM, I'll delete my channel!Yes, I know what I just said. But this is too good!You’ll learn:- How to become an AI PM without any tech experience- 2 main categories of AI PMs and how to break into each- How to build great AI products your users love- All concepts like Evals, RAG, MCP, etc- Creating AI PM portfolioAnd so much more----Brought to you by:The AI Evals Course for PMs & Engineers: You get $800 with this linkMaven: Get $100 off my curation of their top coursesAI PM Certification: Get $500 with code AAKASH25----Timestamps:Two Types of PMs in the World Right Now – 00:00:00How Much AI PMs Make in the US & India – 00:01:49Why Jobs Aren’t Marketed as “AI PM” Roles – 00:04:19Live Slide Share Session Begins (AI PDLC) – 00:05:35What People Get Wrong About AI PM Jobs – 00:08:51Why AI Product Management Is Here to Stay – 00:11:00Product Development Lifecycle Explained – 00:12:35Ad 1: AI Evaluations – 00:14:47Ad 2: Maven Courses – 00:15:46Understanding PM Canals (Idea Sources) – 00:16:34How AI Will Transform the Traditional Product Cycle – 00:18:55The Two Branches of AI: Predictive vs Generative – 00:23:10Diving Deeper into Generative AI – 00:27:17Ad 3: AI PM Certification – 00:28:47How to Build Your Own AI Use Case Database – 00:29:34Why You Shouldn’t Use AI for Everything – 00:34:10Understanding Problem Space vs Solution Space – 00:36:51Most Common Mistake People Make Entering AI PM – 00:40:49The Building Blocks of AI – 00:47:01Contextualization (Prompts, Fine-Tuning, RAG) – 00:50:24The Limitations of AI — Why You Still Need Evals – 00:56:18Case Study: AI-First Job Search Website – 00:58:49Prompt Engineering Breakdown for the Case Study – 01:02:09Understanding and Leveraging AI Agents – 01:03:40Introducing the MCP Framework – 01:08:07How to Build Your AI PM Portfolio – 01:11:37Your Next 7 Steps to Becoming an AI PM – 01:16:04Closing Thoughts and Final Notes – 01:18:41----Key Takeaways:Takeaways:01. Stop Shipping Blind. Your AI product isn't truly valuable until you validate it. Go beyond just building; understand user needs deeply with personas, journey maps, and jobs-to-be-done.02. MOM Test = Your Secret Weapon. The "MOM Test" is about asking questions that even your most supportive friend can't lie about. Don't ask if users "would" use your AI. Ask about their past behaviors and real problems. This helps you define success metrics and avoid building a fancy toy nobody needs. 03. Evaluate Everything, Relentlessly. AI Evals are not just a technical task for engineers, but the most critical tool for Product Managers to build high-quality, trustworthy AI products. Use them to understand, refine, and continuously improve your AI.04. Passion Won't Land the Job. Proof Will. "I'm passionate"...great I guess, but recruiters want to see what you've done. Your portfolio is your direct line to showing you can actually do the job.05. Build Your AI Portfolio. Now. Don't wait for experience. Create product teardowns of AI tools, develop case studies, or launch small side projects. This is your living, breathing proof of thinking and skill.06. Forget the Resume. Add Value. The ultimate job hack? Identify a problem at a target company and propose a solution, or even build a prototype before you apply. This showcases initiative and concrete skills.07. You’re At Fault (Brutal, I Know). Nailing Prompt Engineering is a direct path to better AI outputs. If your AI misbehaves, it's often your fault for unclear instructions. Refine your prompts for smarter, more reliable AI.08. Generic Resumes In The Bin! Forget sending generic resumes into the void. There are three distinct approaches: just a resume, adding a portfolio and cover letter, or the ultimate "Value Add" where you solve a company's problem before applying. 09. AI Will Do Your Dishes (Metaphorically). While AI Agents promise incredible autonomy and action, remember they still need clear goals and defined tasks. So, while your AI PM dream is big, maybe don't expect it to clean your dishes (yet) – stick to email automation for now!10. Don't Trust LLMs Blindly. LLMs are powerful. But they need continuous human validation and evaluation frameworks. Automate grading where possible, but always, always, have a human in the loop for critical judgment.----Check out the conversation on Apple or Spotify and the demo on YouTube.----Related Podcasts:If you only have 2 hrs, this is how to become an AI PMHow to Become, and Succeed as, an AI PM | The Marily Nika EpisodeComplete Course: AI Product ManagementTutorial of Top 5 AI Prototyping Tools----P.S. More than 85% of you aren't subscribed yet. If you can subscribe on YouTube, follow on Apple & Spotify, my commitment to you is that we'll continue making this content better.----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

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• How Linear Built a $1.25B Unicorn with Just 2 PMs

  Nan Yu shares Linear's proven product-building method that helped them took Linear to a $1.25B product with just 2 PMs.You’ll learn:- How top companies like Linear are actually building AI features- Secrets to building a lean product that users genuinely love- Nan Yu’s advice for landing top PM jobs.- The Linear method in detail----Brought to you by:The AI Evals Course for PMs & Engineers: You get $800 with this linkVanta: Automate compliance, Get $1,000 with my linkAI PM Certification: Get $500 with code AAKASH25Maven: Get $100 off my curation of their top courses----Timestamps:Preview – 00:00:00Why are all the GIANTS using Linear? – 00:02:09Linear's Core Functionality – 00:04:07Ad (AI Evals) – 00:04:44Ad (Vanta) – 00:05:44Linear Method: How They Build Product – 00:06:36Principles of the Linear Method – 00:09:16Saying No to Busy Work / Busy Work Is a Result of “Lack of Clarity” – 00:12:14Linear's Roadmap Planning Horizon – 00:14:43Ad (AI PM) – 00:15:06Ad (Maven) – 00:15:53Planning Process – 00:16:41Linear's Take on OKRs – 00:19:13Setting The Bar High – 00:26:06Story: Building The Smallest Version Possible – 00:28:49Public Roadmaps are DANGEROUS – 00:34:20Applying Linear Method to AI Features (Agents as Users) – 00:38:18Mantra: Solve Real Problems for Real People – 00:43:08Landing a Head of Product Role at Linear – 00:46:08Story: How the Initial Contact with Linear Happened – 00:48:16Advice for Aspiring PMs at Linear – 00:49:50Making it to unicorn status with 2 PMs & 40 engineers – 00:53:18Everlane's Pricing Strategy (Cost-Plus) – 00:53:44Shift from B2C Retail to Product-Led B2B SaaS – 00:55:10Can Today's PMs Shift Industries? – 00:57:22Closing Notes – 00:59:49----Key Takeaways:Takeaways:1. Don't Chase AI Hype, Solve Real Pain: Instead of building flashy AI features (that no one needs), Linear observes where users are actually struggling (like juggling many AI tools for async work) and builds direct solutions. It's about fixing real problems, not just jumping on trends.2. Your Users Aren't a Feature Checklist. Listen intently to user feedback, but don't take every request literally. The goal is to understand their core frustrations and goals, not just to say "yes" to every demand that could lead to bloat.3. Purpose Over PR Stunts. Forget building the "loudest possible feature." Linear's core mantra is to solve real problems for real people. Focus on making the most useful thing, and the noise will follow.4. Run Marathons, Not Sprints. Sustainable momentum beats endless "sprints" every DAMN time. Find a comfortable pace you can maintain not just tomorrow, but a year from now. Burnout is a warning sign, not a badge of honor.5. Scope Small, Ship Often. Unshipped code is debt. By making tasks as tiny as possible, you get the reward of shipping constantly. This creates tangible proof of work, gets immediate user feedback, and keeps the motivation high.6. Quality Isn't a Side Quest. Bugs aren't "minor." They're either critical or high priority. A "little bug" seen a hundred times tells customers you don't care. Quality work is baked into every feature, because a feature isn't done until it's high quality.7. Clear Talk, Clear Work. Ban jargon. LITERALLY. Words should have one meaning. If "issue" refers to a task in your product, it only refers to that. This ruthless clarity eliminates misunderstandings and speeds up collaboration.8. Ditch Old-School User Stories. Traditional "As a user, I want X" stories often hide context and slow things down. Everyone understands software now. Cut the BS. Be direct and state the desired outcome clearly. 9. Build to Learn, Not to Delay. Excessive data collection or endless A/B tests shows lack of decision making. Instead, build a smaller version of your idea quickly, put it out there, and let the market tell you if you're right or wrong. Learn fast, move faster.10. Forget Personas, Find Mike. Stop building for abstract personas. Identify the real people you're working with – "Mike from Brex," "Sarah from Acme Corp." Knowing them by name deepens empathy and leads to better solutions.----Check it out on Apple, Spotify, or YouTube.----Related Podcasts:Complete Course: AI Product Discovery"Most Product Managers are Bullsh*t Managers" -2x CPO----P.S. More than 85% of you aren't subscribed yet. If you can subscribe on YouTube, follow on Apple & Spotify, my commitment to you is that we'll continue making this content better.----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

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• The PM’s Role in AI Evals: Step-by-Step

  Today, we’ve got some of our most requested guests yet: Hamel Husain and Shreya Shankar, creators of the world’s best AI Evals cohort.You’ll learn:- Why AI evaluations are the most critical skill for building successful AI products- What common mistakes people are making and how to avoid them- How to effectively "hill climb" towards better AI performanceIf you're building AI features, or aiming to master how AI Eval actually works, this episode is your step-by-step blueprint.----Brought to you by:The AI Evals Course for PMs & Engineers: You get $800 with this linkJira Product Discovery: Plan with purpose, ship with confidenceVanta: Automate compliance, security, and trust with AI (Get $1,000 with my link)AI PM Certification: Get $500 with code AAKASH25----Timestamps:00:00:00- Preview00:02:06 - Three reasons PMs NEED evals.00:04:40 - Why PMs shouldn't view evals as monotonous00:06:23 - Are evals the hardest part of AI products solved?00:07:37 - Why can't you just rely on human "vibe checks"?00:12:11 - Ad 1 (AI Evals Course)00:13:10 - Ad 2 (Jira Product Discovery)00:14:06 - Are LLMs good at 1-5ratings?00:15:45 - The "Whack-a-mole" analogy without evals00:16:26 - Hallucination problem in emails (Apollo story)00:21:22 - How Airbnb used machine learning models?00:23:56 - Evaluating RAG Systems.00:29:52 - Ad 3 (Vanta)00:30:56 - Ad 4 (AIPM Certification on Maven)00:31:42 - Hill Climbing00:35:51 - Red flag: Suspiciously high eval metrics00:39:02 - Design principles for effective evals00:42:42 - How OpenAI approaches evals00:44:39 - Foundation models are trained on "average taste"00:49:36 - Cons of fine-tuning00:51:27 - Prompt engineering vs. RAG vs. Fine-tuning00:53:00 - Introduction of "The Three Gulfs" framework00:56:04 - Roadmap for learning AI evals01:01:41 - Why error analysis is critical for LLMs01:08:29 - Using LLM as a judge01:10:15 - Frameworks for systematic problem-solving in labels01:17:42 - Importance of niche and qualifying clients. (Pro tips)01:18:43 - $800K for first course cohort!01:20:15 - Why end a successful cohort?01:25:49 - GOLD advice for creating a successful course01:33:39 - Outro----Key Takeaways:1. Stop Guessing. Eval Your AI. Your AI isn’t an MVP without robust evaluations. Build in judgment — or you’re just shipping hope. Without evaluation, AI performance is a happy accident.2. Error Analysis = Your Superpower. General metrics won’t save you. You need to understand why your AI messed up. Only then can you fix it — not just wish it worked better.3. 99% Accuracy is a LIE. Suspiciously high metrics usually mean your evaluation setup is broken. Real-world AI is never perfect. If your evals say otherwise, they’re flawed.4. Fine-Tuning is a Trap (Mostly). Fine-tuning is expensive, brittle, and often unnecessary. Start with smarter prompts and RAG. Only fine-tune if you must.5. Your Data’s Wild. Understand It. You can’t eyeball everything. Without structured evaluation, you’ll drown in noise and never find patterns or fixes that matter.6. Models Fail to Generalize. Always. Your AI will break on new data. Don’t blame it. Adapt it. Use RAG, upgrade inputs, and stop expecting out-of-the-box magic.7. OpenAI Doesn’t Get Your Vibe. Their models are average-taste. Your product isn’t. If you want your brand’s voice in your AI, you must define it yourself — with evals.8. Trust LLM Judges... but validate them hard. LLMs can scale your evals — but you still need to verify them against human-labeled data. Don’t blindly trust your judge.9. Your Prompts Are S**T. If your AI is bad, it’s probably your fault. The cheapest, most powerful fix? Sharpen your prompts. Clearer instructions = smarter AI.10. Let AI Teach You. Seriously. LLM judges aren’t just scoring you — they can teach you. Reviewing how your AI fails is the best way to learn what great outputs should look like.----Check it out on Apple, Spotify, or YouTube.----Related Podcasts:Complete Course: AI Product ManagementTutorial of Top 5 AI Prototyping ToolsIf you only have 2 hrs, this is how to become an AI PMCollege Dropout Raised $20M Building AI Tools | Cluely, Roy LeeBolt CEO and Founder on How he Hit $30M ARR in a YearLogRocket CEO and Founder on How to Build a $100M+ AI StartupAmplitude CEO and Founder on Building the Product Analytics Leader----P.S. More than 85% of you aren't subscribed yet. If you can subscribe on YouTube, follow on Apple & Spotify, my commitment to you is that we'll continue making this content better.----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

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• AI Product Discovery: Complete Course

  We could talk a million things with Tanguy Crusson but I’m keeping it to what I like the most about his work - Product Discovery.He shares a rare, behind-the-scenes look at how his team at Jira Product Discovery uncovers real user problems, validates solutions quickly, and avoids wasting time on ideas that won’t land.----Brought to you by:Jira Product Discovery: Plan with purpose, ship with confidenceProduct Faculty: Get $500 off the AI PM certification with code AAKASH25Vanta: Automate compliance, security, and trust with AI (Get $1,000 with our link)AI PM Certification: Get $500 off Miqdad Jaffer (of OpenAI)’s certification----Timestamps:Preview & Intro — 00:00:00Has AI Actually Changed Product Discovery? — 00:00:27Ad: Jira Product Discovery — 00:10:50Ad: AIPM Certification with OpenAI PM — 00:11:45Where AI Truly Helps in Discovery (Real Use Cases) — 00:12:32Watch the Live Demo on YouTube for Full Context — 00:14:34The Discovery Process (Deep Dive)Phase 1 – Wonder: How Atlassian Uncovers Real Problems — 00:16:51Why Tanguy Hasn’t Written a PRD in 5 Years (+ Wild Engineer Story) — 00:21:04How to Lead Great User Interviews (Tips from a UX Researcher) — 00:25:24Ad: Vanta Compliance & Security — 00:28:30Ad: AI Evals for PMs & Engineers — 00:29:34The Right Way to Structure Discovery Documents — 00:30:34How Atlassian Actually Uses Tools for Product Discovery (Full Stack Demo) — 00:33:23Why This System Works Better Than Traditional PM Workflows — 00:41:24Moving Through DiscoveryPhase 2 – Explore: Rapid Prototypes & Real Feedback — 00:41:58Phase 3 – Make: When the Team Commits to Building — 00:49:04“Just Ship It” is Bad Advice — Here’s Why — 00:52:24Can You Trust Feedback from Free Users? — 00:56:00Phase 4 – Impact: Measuring Real-World Results — 01:07:33How to Build Real Trust as a Product Manager — 01:14:41PMs Want to Keep Engineers Busy: Good Strategy or Trap? — 01:17:46Aakash: “I Wish I Talked to You 10 Years Ago”, Here’s Why — 01:22:46Closing Reflections & Takeaways — 01:26:33----Key Takeaways:1. Discovery isn’t a phase, it’s a system. Atlassian runs product discovery continuously, not just “before development.” It’s embedded across problem finding, prototyping, building, and post-launch.2. Use video, not documents, to communicate user pain. Instead of writing long research summaries, PMs compile 10-minute reels of real customer interviews. Watching raw emotion builds urgency and alignment.3. Start with ~10 users — not thousands. Atlassian validates ideas with small, focused user groups. It's faster, cheaper, and more revealing than wide surveys or launches.4. Prototype with whatever is fastest. From AI tools like V0 to basic Figma slides, the goal is speed. You don’t need polished UIs — you need fast feedback on core concepts.5. Strong user reactions guide investment. When users say “I need this now,” that’s a green light. Mild interest or polite nods? That’s a warning to dig deeper.6. Build only once you have real pull. They don’t move into development (“Make” stage) until a prototype has strong qualitative validation. Code follows conviction.7. PMs rotate weekly to tag and analyze feedback. Every week, one PM owns triaging incoming feedback, tagging it to ideas, and surfacing themes. Discovery is part of the rhythm — not a side project.8. Real discovery requires exposure, not summaries. Dashboards, sanitized reports, and secondhand quotes are not enough. PMs must stay close to raw user input — live or recorded.9. Post-launch reflection is mandatory. Every shipped feature goes into the “Impact” phase. They assess: is it working? Should we scale, refine, or kill it?10. Discovery is a team sport. PMs, designers, engineers, even sales, everyone participates in interviews, watches clips, and shapes the product. It’s not just a PM’s job.----Check it out on Apple, Spotify, or YouTube.----Related Podcasts:How ZoomInfo IPO’d - With CEO/ Founder Henry ShuckHow LogRocket Became a $100M+ AI Company - With CEO/ Founder Matt ArbesfeldHow Amplitude Became the #1 Product Analytics Tool - With CEO/ Founder Spenser SkatesHow Zoom Took Over The World - With Zoom's Meetings Product during Covid ( now leads their Events & Webinars product)----P.S. More than 85% of you aren't subscribed yet. If you can subscribe on YouTube, follow on Apple & Spotify, my commitment to you is that we'll continue making this content better.----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

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• This $20M AI Founder Is Challenging Elon and Sam Altman | Roy Lee, Cluely

  Amazon banned him. Ivy Leagues kicked him out.And still, he went mega viral, built a $6M ARR AI startup, and raised $15M from a16z… all in a matter of weeks.This might be the craziest founder story you’ll hear all year.If you’re building in a competitive market, struggling to stand out, or just want to learn how to blend controversy with growth, this one’s for you.----Brought to you by:AI PM Certification: Get $500 with code AAKASH25Jira Product Discovery: Plan with purpose, ship with confidenceThe AI Evals Course for PMs & Engineers: You get $800 with this link----Timestamps:21yo Worth $100M+ - 0:00Harvard Kicked Me Out - 3:02Tech Twitter Main Character - 4:33Ads - 6:44Controversial = Views Formula - 8:26Stripper Commercial Brainstorms - 12:47Liquid Glass Before Apple - 16:55User Feedback Drives Product - 19:48Sales Tech & Enterprise - 23:16Cheating in Meetings - 27:19Ad - 28:22How to Fundraise Like Roy - 29:28Brain Chips End Game - 31:05Roy vs Elon vs Sam - 33:12Frat House Culture - 34:44The Cluely Internship - 37:37Are We Getting Dumber? - 38:51Roy Going to Jail? - 40:44Thanks for Watching - 42:18----Key Takeaways:1. Don’t wait for permission. He got kicked out of two top schools and used that energy to build something the world couldn’t ignore. If the system doesn’t reward you, build outside it.2. Design for real behavior, not rules. Interview Coder wasn’t legal or polite, it was effective. It gave users AI help without getting caught. Start with what people actually want.3. Rethink how AI should show up. Stop building chatbots. Build experiences where AI quietly blends into the workflow. Think overlays, not windows. Think invisible, not interruptive.4. Your product doesn’t need to sound safe. “Cheat on everything” wasn’t just a headline, it was a magnet for attention. Don’t fear being bold if it reflects what your product actually does.5. You don’t need a pitch deck if the story tells itself. Cluely raised $15M without running a process. When the traction is undeniable and your product is everywhere, investors come to you.6. Build virality into your operating system. Don’t “hope” something goes viral. Study what’s working. Run daily idea sessions. Create with the expectation that every post could hit 100M views.7. Make your content pass two filters or kill it. Can anyone understand it instantly? Will people feel something strong enough to react? If not, it won’t break through. Keep it simple and emotional.8. Ignore vanity metrics, chase visibility. Don’t waste time measuring click-through rates on content that won’t work next week. Stay focused on showing up everywhere your user lives.9. You don’t need a big team to move fast. Cluely runs on 4 engineers. No designers. No PMs. Still they've $6M ARR. If you can ship fast and learn faster, you’re already ahead.10. Build a product that feels inevitable. This isn’t just for sales teams. Cluely is betting on a world where AI support shows up before you even ask for it. Design for that future now.----Check it out on Apple, Spotify, or YouTube.----Related Podcasts:Bolt CEO and Founder on How he Hit $30M ARR in a YearLogRocket CEO and Founder on How to Build a $100M+ AI StartupAmplitude CEO and Founder on Building the Product Analytics Leader----P.S. More than 85% of you aren't subscribed yet. If you can subscribe on YouTube, follow on Apple & Spotify, my commitment to you is that we'll continue making this content better.----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

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