Product Growth Podcast

Today’s EpisodeAI PM jobs pay 30-40% more than regular PM jobs.But here’s the problem: You can’t just slap “AI PM” on your resume.Todd Olson has spent 28 years in product management, VP of Product at a public company, then founder of Pendo, now a $2.5B product management platform working with everyone from American Cancer Society to Zendesk.----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----Key Takeaways1. AI PM market exploded - Last year 10% of PM jobs were AI PM jobs. This year it's 20%. They pay 30-40% more because of scarcity and skill level. But Todd warns: "You better damn well be good and know what you're talking about if you're gonna call yourself an AI PM because we are going to interrogate the hell out of it."2. Real requirement is production at scale - Not "I built prototype at 1-person startup." Hiring managers want 20,000 paying B2B customers experiencing your AI feature successfully. To get there: upskill internally at current company by shipping AI features on your roadmap.3. The 5-layer technical pyramid - Foundation: AI/ML fundamentals, data pipelines, prompt engineering. Middle: Observability (trace analysis), cost optimization, evals. Top: Product strategy, stakeholder management, leadership. You need to climb all 5 layers. Most PMs stop at layer 1.4. RAG is table stakes - "RAG is the de facto way to build." You ingest data, create embeddings, feed into vector database, look up relevant context, pass to LLM. Todd: "If you put too much in context window, just like a human, you get confused. You want to give the right context."5. PM-engineering tension is real - At startups, PMs do trace analysis. At large companies, engineering managers push back: "This is my world. I don't want some PM shadowing me." Similar to Data Dog—most PMs don't have login. Know the line. Be fluent but respect boundaries.6. But evals are YOUR domain - Unlike trace analysis, evals are where PMs are the expert. "The PM is probably the best-suited human being to author and manage eval sets." You understand user and business needs. Engineers don't have that context. This is must-have competency now.7. Cost optimization will matter - Some AI companies have sub-15% gross margins. Traditional software is 70-80%. Todd: "It's not a business at sub-15%." Eventually you'll rearchitect systems because infrastructure is too costly. Rule: when something's faster, it's cheaper (both buying compute).8. Solve hard problems, not shiny objects - Todd's test: "Are we gonna do much better job than ChatGPT out of box? Why would we just wrap that and slap Pendo logo on it?" His discovery agent example: hard part isn't interviewing customers—it's finding which to interview, prioritizing, scheduling. Automate that workflow.9. Kill bad features ruthlessly - Todd shipped features couple years ago that weren't great and turned them off. "Too often we hold on to something. Turn them off. Be unafraid. The more stuff in your product, the worse the experience is by default."10. Control the narrative with boards - Don't show up with no story and get crushed with random requests. Todd: "Show them how you actually run your business. I want to see what you're looking at, not something just made for me." Think deeply about how each bet drives shareholder value.----Where to Find Todd Olson* LinkedIn* Company* X----Related ContentPodcasts:* How to Become, and Succeed as, an AI PM | The Marily Nika Episode* If you only have 2 hrs, this is how to become an AI PM* Complete Course: AI Product ManagementNewsletters:* How to Become an AI Product Manager with No Experience* How to Write a Killer AI Product Manager Resume* How to become an AI Product Manager----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

Today’s EpisodeThere are tons of tutorials about Claude Code and Cursor for IC PMs.But what about leaders?Today’s episode is a masterclass on both sides of AI product leadership. How to be a productive AI leader and how to ship AI-native features at scale.Rachel Wolan is the Chief Product Officer at Webflow, the $4 billion company powering TED Talks, SoundCloud, and Reddit.Rachel walks through building her agentic Chief of Staff live, sets up a LinkedIn post generator from scratch, and shares the brutal lessons from launching Webflow’s AI app generator.----Brought to you by:Linear: The task management platform dethroning Jira-----Key Takeaways:1. IC CPO means self-serving answers - "As a leader, you are able to get your own answers to practically any question." No waiting on data scientists. No back-and-forth with analytics. You have tools to self-serve insights, make analysis, automate workflows. Model behavior for your team to inspire them.2. Calendar agent analyzes time - Runs weekly with prompt: "Analyze my calendar for last two weeks. Where could I delegate?" Returns delegation opportunities, red flags (double bookings, context switching), what to cut next week. Rachel gives output to EA. Spot on when shown live.3. Email agent watches behavior - Complete inbox access. Runs triage, archives junk (calendar notifications, marketing), pins important messages, creates draft replies. Twist: watches behavior. If email sits too long, it notices. Caught meeting missing link. Rachel's rule: agent recommends, she approves. No autonomous sending.4. Analytics agent via MCP - Connected Claude Code to Snowflake via MCP servers (not officially supported repos, just fed them to Claude Code). Ask natural language questions, get SQL executed real-time. "How many sites does Shirts.com have?" Claude writes query, authenticates via SSO, returns answer. Data scientist in pocket.5. Accept the adoption curve - Your org follows standard curve: early adopters, early majority, late adopters, laggards. Create pathways for everyone to ascend ladder at their pace. Don't force everyone to be you. Rachel to team: "I only want to see prototypes when you have meetings with me." Creates culture investing in prototype quality.6. Builder Days strategy - Give everyone access: Claude Code licenses, MCP to Snowflake/Tableau, Figma Make, Cursor with design system. Run Builder Days where champions help others through technical hurdles. Everyone demos something outside comfort zone. Results: 0% to 30% of designers using Cursor weekly after first Design Builder Day.7. Rewrite career ladder - Webflow rewriting career ladder to make AI-native work an expectation, not nice-to-have. Create right incentives. Make sure people supported. Avoid AI for AI's sake. Example: Two designers built similar prototypes. Director caught early: "Go harmonize your prototypes now." Easier now than late in product cycle.8. MVO before MVP framework - Most teams: Feature → PRD → Design → Ship. Rachel flips it. MVO (Minimal Viable Output) before MVP. Get model's output right FIRST using RAG, prompt engineering, context engineering. Only then build feature. "If you don't have desired outputs, don't spend time productizing the AI feature."9. Evals are now your job - Brutal story: Webflow's AI app generator 2 weeks from launch. Rachel tested it. Agent kept dying. Realized: changed underlying model, evals didn't have coverage. Evals = test cases for models. Want dream evals (should pass) and edge cases (should fail). Use BrainTrust. Teaching PMs to write evals is part of AI PM toolkit now.10. Build on your strengths - Framework: See trend → Is it applicable to customers? → What's YOUR core competency? Webflow's strength: bringing visitors to front door via CMS. Built production-grade app generator (not prototype like Lovable). Uses your brand, CMS, hosting, security. "We're bringing a way to prompt an app to production." Don't copy trends, leverage unique strengths.-----Where to Find Rachel Wolan * LinkedIn* Website* X----Related Content* Claude Code Tutorial for AI PMs* AI Agents for PMs in 69 Minutes, with IBM VP* 5 AI Agents Every PM Should Build, with CEO of LindyNewsletters:* AI Evals Guide for PMs* Prompt Engineering for AI Agents* AI Agents: The Ultimate Guide for PMs----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

Today’s EpisodeWhy do your prompts keep failing?You write the perfect prompt. The AI spits out garbage. You tweak. You iterate. You spend hours getting mediocre results.XK built Cues to $10M ARR in 60 days with zero VC funding and zero advertising. Today, he’s dropping the complete playbook:-----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:Reforge http://reforge.com/aakash-----Key takeaways:1. Context engineering beats prompting - One prompt won't work. Like hiring someone who knows nothing about your company—impossible to get results in 5 seconds. Accumulate context, build knowledge base, let AI know you over time. Combines system prompts, user prompts, memory, and RAG.2. The Mom analogy - Your mom knows your preferences, goals (grow taller for basketball), what makes you happy. She doesn't need detailed instructions. That's context engineering. AI that knows you creates better results and positive loops.3. Threads growth hack - Created hundreds of accounts posting use cases daily. Zero ad spend. Why it works: Threads gives traffic generously, less crowded than X, no creator hierarchy. Result: 3M impressions/month, hundreds of daily visits. Targeted Taiwan/Hong Kong markets.4. MVO before MVP - Traditional: Feature → PRD → Design → Ship. Xiankun's way: Get model output right FIRST. Use RAG, prompting, fine-tuning for Minimal Viable Output. Then productize. "If no desired outputs, don't spend time productizing."5. Visual context engineering - Use spatial tools: draw squares, graphs, sketches. AI understands spatial relationships. Unlike ChatGPT where files disappear, Kuse gives 2D space to store/reuse. Graphic operating system for AI that compounds.6. The pivot story - Started as design agent. Users uploaded documents instead. Knowledge base usage far exceeded design. Pivoted to horizontal knowledge-based AI. Listen to your users.7. Why X sucks for growth - Structured creator hierarchy. Can't farm traffic without famous connections. Good for VC fundraising, terrible for user acquisition. Threads and Instagram are underserved with real users.8. Compounding context power - Regular chatbots: one-off, context disappears. Kuse: processes files when you're away, pre-prepares everything. Like having ingredients ready vs ordering each time. Each interaction improves.9. Trading company origin - Co-founded YC company, created trading company, made money, funded Kuse with profits. Built without VC pressure. "Entrepreneurship is a game of focus." Building without chasing VC gives fresh perspective.10. Future vision: productivity playground - "Not building productivity tool, building playground." When AI takes jobs (2030-2040), people need fulfillment. Kuse is amusement park where people pretend to work, feel satisfaction. Going to pure pleasure, not efficiency.----Where to Find Xiankun Wu* LinkedIn* Threads* Company----Related ContentPodcasts:* We Built an AI Employee in 62 mins* Conversation with the CEO and Founder of Bolt* This $20M AI Founder Is Challenging Elon and Sam Altman | Roy Lee, CluelyNewsletters:* Context Engineering Guide* Prompt Engineering in 2025* How to become an AI Product Manager----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

Today’s EpisodeThe salary for AI PMs is skyrocketing.Hamza Farooq works with companies like Home Depot, Trip Adviser, and Jack in the Box on their AI strategy. He teaches AI PM courses at Stanford, UCLA, and Maven.Today, he’s giving you the complete 6-month roadmap to go from no experience to PM at OpenAI or Anthropic.We built a live AI prototype in 30 minutes (with RAG and agents working). And Hamza breaks down the exact technical skills you need to master.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:* Maven* Amplitude: The market-leader in product analytics* Vanta: Leading AI compliance platform* NayaOne* Kameleoon: Leading AI experimentation platform----Key Takeaways:1. AI PM salaries are skyrocketing - The median total comp for AI PMs is rapidly increasing. But now you need technical depth. Previously, you didn't need to know what RAG is or how fine-tuning works. Now you have to be a jack of all trades.2. We built a working prototype in 30 minutes - Live demo: Lovable for front-end + n8n for workflow automation + RAG connected and working. What used to take days now takes minutes. This is the power of modern AI PM tools.3. Context engineering is more important than prompt engineering - Prompt engineering is what you tell an LLM. Context engineering is how you design the instructions. You combine: system prompt, user prompt, memory (long-term), and RAG. This enables true personalization.4. Know the difference: fine-tuning vs RAG - Fine-tuning = adding new vocabulary (new words). RAG = adding new knowledge (new information). Use RAG for knowledge that changes frequently. Use fine-tuning for vocabulary or specialized response patterns.5. The 5-step architecture you need to master - Step 1: Understand what LLMs are. Step 2: Learn how to build applications. Step 3: Master prompt engineering. Step 4: Implement RAG systems. Step 5: Build agentic systems. Follow this roadmap on repeat.6. Use the three-wave approach for building - Wave 1: Save time (efficiency gains). Wave 2: Better quality (better output). Wave 3: Completely new (novel capabilities). Start with time-savers, progress to quality improvements, end with breakthrough innovations.7. Ask yourself 3 questions before building anything - Does it solve a user problem? Does it solve an organizational problem? Does it align with your business model? If yes to all three, build it. This validates every project.8. Build-first mentality wins - Don't just follow roadmaps. Keep building things. You have to learn by doing. The best way to become an AI PM is to build 10+ projects and see where your products fit in solving real business problems.9. Real-world example: Traversal.ai - Hamza's company works with manufacturers (Amazon suppliers, Jack in the Box, Home Depot). They built an army of agents processing 20,000 SKUs daily with demand forecasts. Results: better inventory optimization, planning, and cost savings.10. Teaching accelerates your own growth - Hamza makes 10-15% of revenue from Maven courses. Why keep teaching? "I teach because I grow." His foundation course builds empathy with users. His developer course uplifts his technical skills by working on real problems with senior engineers.----Where to Find Hamza Farooq* LinkedIn* NewsletterRelated ContentPodcasts:* Google AI PM Director drops an AI PM Masterclass* If you only have 2 hrs, this is how to become an AI PM* Complete Course: AI Product ManagementNewsletters:* How to Become an AI Product Manager with No Experience* How to Write a Killer AI Product Manager Resume* How to become an AI Product Manager----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

Today’s EpisodeEvery PM needs to master AI prototyping in 2025.But which tool should you use? And how do you actually prototype effectively?Alex Danilowicz built Magic Patterns to $1M in revenue in 6 months. Today, we’re putting his tool against the competition live.We built the same prototype in 5 different tools and graded each one. Then Alex shared the exact workflow his customers use.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:Vanta: Leading AI compliance platformTestkube: Leading test orchestration platformKameleoon: Leading AI experimentation platformJira Product Discovery: Plan with purpose, ship with confidenceThe AI PM Certificate: Get $550 off with ‘AAKASH550C7’----Key Takeaways1. Different tools for different jobs - Magic Patterns excels at visual prototyping, user research, and design system integration. V0/Replit/Bolt excel at full-stack functionality, real APIs, and backend. We tested 5 tools live—V0 won (3.7 GPA), Magic Patterns second (3.6 GPA).2. Define your end goal before opening any tool - Sharing with customers = need design system. Internal validation = skip brand context. Alex's mistake in our face-off? He jumped into building without setting up his preset and wasted time retrofitting ChatGPT's Agent Kit styling later.3. Set up your design system in 5 minutes - Magic Patterns Chrome extension grabs components from Storybook, production sites, or Figma. Click "Convert to Component" and it's available in every prompt. Converts HTML to Tailwind automatically. 5 minutes upfront saves hours later.4. Gather context before prompting - Don't start with blank prompts. Common sources: Jira tickets, PRDs, competitor screenshots, customer feedback. Power users use ChatGPT/Claude to write their Magic Patterns prompts first.5. Use select mode for iterations - Vague prompts waste time. Bad: "Make it better." Good: "Move toast to top-left and make it green." Always click the exact element you want to change. The AI can't read your mind.6. The new product development workflow - Old: Write PRD → Align stakeholders → Build → Pray. New: Build prototype (30 min) → Share link → Test with customers → Iterate → Write PRD with learnings → Build validated solution. Cuts 15+ meetings down to 1.7. AI prototyping cuts failure rates in half - 80% of features don't hit their metrics. You're building blind. With prototypes, you validate: usability, viability, value, drop-offs, corner cases. Before: only test biggest features. Now: test every feature.8. Break out of doom loops - Pattern to avoid: "Doesn't work" repeated 10 times. Repeating the same prompt makes it worse. Use Magic Patterns' /debug command or restart with clearer prompt. Read the AI's output—it's having a conversation.9. Master the 4-step workflow - Step 0: Define end goal. Step 1: Set up design system (if needed). Step 2: Gather context (PRDs, screenshots). Step 3: Iterate specifically with select mode. This workflow helped Magic Patterns hit $1M revenue in 6 months.10. Know when to use each tool - Magic Patterns finished first in speed with best iteration quality. Replit prompted for OpenAI key (more functionality). Use Magic Patterns for: user validation, testing interactions. Use V0/Replit for: backend, real APIs, deployable prototypes.----Where to Find Alex Danilowicz* LinkedIn* Twitter/X* Website----Related ContentPodcasts:* Cursor Tutorial* Windsurf Tutorial* AI Prototyping TutorialNewsletters:* AI Agents: The Ultimate Guide for PMs* Ultimate Guide to AI Prototyping Tools* How to Land a $300K+ AI Product Manager Job----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

Today’s EpisodeWhy do some AI products feel like magic while others feel like work?You shipped. It works. Your metrics show “success.”But users aren’t coming back. They’re not telling friends. And next quarter, they’ll switch to the competitor with a better model.Nesrine Changuel built Spotify Wrapped and ran Google’s Delight Team. Today, she’s giving you the complete playbook:The 4-step Delight Model to engineer emotional connection (not just satisfaction)----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:* Miro: The AI innovation workspace* Vanta: Leading AI compliance platform* Testkube: Leading test orchestration platform* Kameleoon: Leading AI experimentation platform* The AI PM Certificate: Get $550 off with ‘AAKASH550C7’-----Key Takeaways01 | Understand the 3 Types of DelightLow Delight solves functional needs only. Surface Delight adds emotion without function (confetti, animations). Deep Delight combines both - solving problems while creating emotional connection. ChatGPT and Cursor win because they nail Deep Delight. Most PMs only ship Low Delight.02 | Follow the 50-40-10 RuleAllocate 50% of your roadmap to Low Delight (core functionality), 40% to Deep Delight (differentiation), and 10% to Surface Delight (brand personality). Deep delight drives 2x retention, 2x referrals, and 2x revenue versus satisfied users. This is your competitive moat.03 | Start with Motivational SegmentationStop segmenting by demographics. Identify WHY users actually use your product. Map functional motivators (search, get inspired) AND emotional motivators (feel less lonely, feel proud). Your users aren't all using your product for the same reason.04 | Use the Delight GridCreate a grid with functional motivators on vertical axis and emotional on horizontal. Place every feature idea on it. Only functional = Low Delight. Only emotional = Surface Delight. At the intersection = Deep Delight. Can't map it? Don't build it.05 | Apply the Humanization TechniqueAsk: "If my product was a human, how would the experience be better?" Google Meet compared to being in the same room, not Zoom. Dyson compares to hiring a human cleaner, not competitors. This creates features like hand raise and emoji reactions.06 | Validate with the Delight ChecklistBefore shipping, ask: Does it bring value to business AND user? Is it inclusive? Is it familiar? Is it continuous? Is it measurable? Google Meet held back filters until they worked on ALL skin tones. This prevents Apple's breakup message disaster.07 | Study Deep Delight ExamplesGmail Smart Compose reduces stress while helping you write. Google Meet's AI translation uses YOUR voice and emotion. Spotify's Discover Weekly personalizes while creating belonging. Chrome's Inactive Tabs improves performance while respecting user relationships. Function + emotion together.08 | Test for Corner Cases ObsessivelyApple's AI summarized a breakup as "no longer in relationship, wants belongings." WhatsApp told a grieving person to "ask John to resend" a photo of her deceased brother. AI progresses fast functionally, but emotional needs get ignored. Corner cases destroy reputations.09 | Learn from ChatGPT's WinChatGPT has 800M users not because of accuracy. People pay subscriptions because they feel less lonely. The emotional need - companionship for solo founders and remote workers - drives retention. Deep delight = personalization that improves over time and remembers context.10 | Start Delight Early, Not LaterDon't say "let me ship functionality first, add delight later." You're building brand perception from day one. Users forgive functional gaps if the experience delights. They won't forgive boring products that work. Engineer delight from the start.----Where to Find Nesrine Changuel* LinkedIn* Twitter/X* Product Delight Book----Related ContentPodcasts:* How to Use Google’s Latest AI Tools* What it means to be Design-Led* If you only have 2 hrs, this is how to become an AI PMNewsletters:* How to Build AI Products Right* How to Land a $300K+ AI Product Manager Job* How to Become an AI Product Manager with No Experience----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

Today’s EpisodeAlex Reachvky has helped hundreds of PMs land $700K+ AI jobs.The gap between $140K and $700K isn’t magic. It’s method.Today, he breaks down the exact AI-powered workflow to land an AI PM job, from resume creation to acing interviews.This is the playbook PMs are using right now to 10X their callbacks and land multiple offers.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:Brought to you by Linear: Plan and build products like the best.----Key Takeaways1. AI PM jobs pay 30-40% more than regular PM roles - Group PMs make $360K-$600K, CPOs make $2M+. In 2025, 20% of PM roles now mention AI (up from 2% in 2023). The market is exploding and compensation bands are wider than ever.2. Your resume's top 3 lines are everything - Recruiters spend 7 seconds scanning for Impact, Scope, and Recognizability. Template: "[X years] PM at [Google] | [2B users] | [Scaled revenue 50% YoY to $3.5B]." Pack your biggest wins and recognizable brands here.3. Create a "bullet vault" then customize in 5 minutes - Use AI to transform your raw career dump into structured bullets covering all PM skill bundles. Use this master resume to tailor for each role by extracting 3-5 non-generic must-haves from the JD.4. Cold applications get 1% callbacks, outreach gets 10-15% - The math: 30-100 apps → 3-4 callbacks → 1 interview. For every role, find the hiring manager, recruiter, and senior PM. Use ContactOut for emails. Message formula: 1 intro + 3 bullets + 1 CTA under 150 words.5. Follow up on days 2, 3, and 5 - People miss emails. Persistence wins jobs. If no email response, send LinkedIn connection request. The golden age of PM networking is here: send 30 connection requests daily, comment on posts for 10X more reach than posting.6. Use Whisper to brain dump at 200 WPM - Answer 24 career questions by speaking instead of typing at 120 WPM. Cover: projects, impact, obstacles overcome, tools introduced, people mentored. This becomes your career vault for both resumes and behavioral interview stories.7. Behavioral interviews follow Hook-Principles-Action-Results-Learnings - Build 10-15 stories covering leadership challenges, stakeholder conflicts, failed projects, launches. Practice progression: Written first → Spoken → Timed (under 3 minutes). Feed to AI for refinement and probing follow-ups.8. Case interviews are evaluated on 6 dimensions - Structured Thinking, User Focus, Product Sense, Prioritization, Communication, Creativity. Prompt AI: "You're a FAANG interviewer. Ask me ONE question. Rate 1-5. Quote my weak phrases, explain why they failed, give better approach."9. Only apply when 50%+ aligned with the role - Extract non-generic must-haves from the JD using AI. Ignore "team player" fluff. Focus on: specific tech infrastructure, growth levers, scale requirements. Rewrite top 3 lines and stack rank bullets to match. Don't let AI fabricate experience.10. Build your target company list strategically - AI prompt: "Create 50-100 companies ranked by fit. Consider: size (public/late-stage/early-stage), interests, geography." Keep broad. More interviews = better negotiation leverage. Focus on roles posted in last 24 hours. Most PM jobs still in SF Bay/Seattle.----Where to Find Alex Rechevskiy* LinkedIn* Twitter/ X* Website----Related ContentPodcasts:* Google AI PM Director drops an AI PM Masterclass* If you only have 2 hrs, this is how to become an AI PM* Complete Course: AI Product ManagementNewsletters:* How to Become an AI Product Manager with No Experience* How to Write a Killer AI Product Manager Resume* How to become an AI Product Manager----PS. 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

Every PM is asking the same question: Which AI tools actually make me faster?There are hundreds of apps. Most are hype. Some are game-changers.Today, I sat down with Anshumanni Rudra - VP of Product at Hotstar, now Group Product Manager at Google leading all APAC payments - to rank 70+ AI tools tier-list style.We didn’t hold back. S-tier tools got crowned. D-tier tools got exposed.And we revealed the single best AI tool for product managers in 2025.Watch the full episode for a chance to win a 1-year free subscription to my newsletter.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:* Miro: The innovation workspace* Vanta: Leading AI security & compliance platform* Testkube: Leading test orchestration platform* Kameleoon: Leading AI experimentation platform* Dovetail: abc----Key Takeaways1. Claude Code is the absolute best AI tool for PMs - Anshumanni runs 6 terminal windows simultaneously doing different things on different parts of his directory. It understands your entire codebase and lets you go from idea to working code in minutes.2. superwhisper is the S-tier dictation tool that has Anshumanni shouting debugging commands at his screen like Tony Stark. His typing speed has actually fallen since he started dictating everything, making typing feel obsolete.3. Lindy.AI is the S-tier agent builder PMs actually want because you can prompt it with natural language instead of building flows. Create email responders, meeting prep assistants, and podcast-to-blog converters without touching code.4. Replit is the ultimate AI prototyping champion that can plan and work for hours building complete applications with minimal guidance. Even before AI, Replit was a strong web-based IDE with deep developer understanding that shows.5. Granola is the S-tier meeting tool that learns your style and auto-generates talking points based on previous conversations. Unlike Otter or Fireflies, it has intelligent context awareness like a personal assistant.6. Perplexity gets C-tier as Anshumanni's usage has "gone down quite drastically" since the early days. AI mode in other tools now does what Perplexity used to do with deep search rabbit holes.7. Cursor gets A-tier as the only IDE with the agent on the right side of the screen, which matters for how PMs think. Anshumanni's usage is "way higher" than other tools purely because of this UX choice.8. Bolt is Anshumanni's pick for best AI prototyping in A-tier with the best structure from the start. It thinks about both front-end and back-end by default, letting you go from prompt to deployed app in minutes.9. GitHub Copilot is the first D-tier tool because it's "just not as good" - very ChatGPT focused, not enough Claude. Developers are leaving for Cursor and Claude Code for a reason.10. Don't chase shiny tools - analyze how you spend your week, find what takes the most time, then find the specific tool that solves that problem. Pick tools for your workflow, experiment, then measure if they improve productivity.---Related ContentPodcasts:* How to Use Google’s Latest AI Tools | Jaclyn Konzelmann Episode* How to PM Production Changes with Devin | Sahil Lavingia Episode* Complete Course: AI Product ManagementNewsletters:* How to Become an AI Product Manager with No Experience* How to Write a Killer AI Product Manager Resume----Want my coaching to your dream AI PM job? Apply to grab one of the remaining 17 seats in my cohort:P.S.1 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.P.S.2 I’d really appreciate ratings + reviews on podcast platforms as well.----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

I had a precious hour of a Google AI PM Director’s time. So, I extracted all the best insights about AI PM for you:How to use Google’s latest AI tools like an insiderHow to build great AI productsHow to become an AI PMAnd I didn’t hold back on the tough questions. And Jaclyn Konzelmann dropped an absolute masterclass.You don’t want to miss her advice on AI PM resumes...----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:Vanta. Pendo. Linear Generic. Jira Product Discovery.* Vanta: Leading AI security & compliance platform* Pendo:* Linear: Plan and build products like the best.* Jira Product Discovery: Plan with purpose, ship with confidence* LandPMJob: Land a PM Job with Aakash Gupta----Key Takeaways1. Nano Banana Understands World Models: Ask it to show Toronto in winter → adds snow. San Francisco in winter → no snow. The model knows SF doesn't get snow. This world knowledge unlocks creative workflows beyond basic image generation.2. The Colorization Workflow: Use Gemini Pro to refine prompts → Focus on vibrant colors, lighting transformation, hyperrealistic detail, modern camera optics → Add negative prompts for failed iterations. "Keep playing around with things until you get it just right."3. Chain Tools for Advanced Workflows: Photo → Imagen (reimagine as drone show) → Veo (animate the drones flying) → Result: Your pet as a living drone show with tail wagging. Access through AI Studio, Gemini app, or Mixboard.4. Build AI Apps Without Code Using Opal: Describe what you want in natural language → Opal writes the prompt chains → Customize models and outputs → Share publicly. Examples: Resume critique tool, nature collage generator, custom storybook maker.5. The Anatomy of an Agent Framework: Every AI agent has 3 components - Models (text/image/video capabilities), Tools (APIs, search, UI actions), Memory (what to remember, personalization strategy). Define these before writing code or PRDs.6. The User Interaction Spectrum: Every AI product falls on "Do it FOR me" (Deep Research, Audio overviews that run and return) vs "Do it WITH me" (vibe coding, interactive experiences).7. The Inverted Triangle: Think Big, Ship Fast: Think REALLY big → Use 3 levers to ship: Scope (ruthless MVP cuts), Positioning (beta/experiment labels), Audience (internal → trusted testers → public). Don't let process slow the vision.8. Ask The Paradigm Shift Question: Are you building a faster horse or a car? Process-improving a workflow or creating an entirely new one? "The real value is the unlock on what's the new way things will get done."9. The Future-Proofing Question: What happens when models get better? Real example: Mixboard threw out months of image editing work when Nano Banana launched with natural language editing.10. Google's 6 Hiring Criteria for AI PMs: Exceptional product taste, visionary leadership (think 5 steps ahead), clarity in chaos, compelling product storytelling, full-spectrum execution (blended role profiles), deep AI intuition. Keep resume to 1 page, show actual work, design with personality.11. The Side Project Strategy: Run 10 side projects simultaneously. Not to launch 10 products, but to think differently and connect dots.12. Don't Get Precious About Ideas: Any single idea can get commoditized in weeks with AI. The skill isn't having one great idea—it's consistently generating good ideas.----Where to Find Jaclyn Konzelmann* X (Twitter)* Linkedin* Substack----Related ContentPodcasts:* How to Become, and Succeed as, an AI PM | The Marily Nika Episode* If you only have 2 hrs, this is how to become an AI PM* Complete Course: AI Product ManagementNewsletters:* How to Become an AI Product Manager with No Experience* How to Write a Killer AI Product Manager Resume* How to become an AI Product Manager----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

Today’s EpisodeSam Altman said one person will build a billion-dollar company.Sahil’s already halfway there with just one employee.Most PMs are still running 6-week sprints. Writing 10-page PRDs. Coordinating between designers and engineers. Sahil ships features from Slack to production in 30 minutes.Here’s the exact AI workflow powering Gumroad’s $10M ARR:If AI gets it wrong, your communication was unclear.----Check out the conversation on Apple, Spotify, and YouTube.Brought to you by:* Vanta: Leading AI security & compliance platform* Testkube: Leading test orchestration platform* Kameleoon: Leading AI experimentation platform* The AI PM Certificate: Get $550 off with ‘AAKASH550C7----Key Takeaways1. Three-Tier AI Workflow: Small tasks (Slack → Devon → Production), Medium tasks (GitHub issue → GPT for PRD → V0 prototype → Ship), Large tasks (4-line brief → V0/Codex → Vercel → Cursor → Production). Match the tool to task complexity.2. From Slack to Production in Minutes: Customer reports feature request in Slack with screenshots. Type "Devon, address this." Devon reads thread, writes code, opens PR, ships to production. "Weeks of coordination at big companies. We just decide and Devon addresses it."3. The PRD Is Dying: Stop writing 20-page PRDs for AI. Write 4 lines. Let AI prototype. See what it misunderstands. That reveals what you forgot to specify. "The PRD is only as dense as what cannot be inferred naturally."4. Use AI to Refine Your Thinking: Paste brief into V0, GPT, and Codex. Each builds something different. Their mistakes show your communication gaps. It's a fake conversation with engineers that makes your real spec better.5. Architecture Is the New Competitive Advantage: Gumroad is deleting 5,425 lines of CSS to migrate to Tailwind (181 lines). Global CSS means every change affects 300 files. Tailwind means one file change. "Devon made a one-file change. With CSS, you're testing 300 files."6. Tailwind Is Built for AI: Design system in 181 lines: fonts, colors, padding, borders, shadows. AI never guesses. Industry standard with massive training data. "It's like hiring an engineer who already understands 2x4s. AI knows exactly what to do."7. AI Is 99th Percentile at Most Things: Defer design and code decisions to AI. If it's important, put it in the spec. If not in the spec, let AI decide. "The decisions AI makes are pretty good. That's why we can move super fast."8. Work on 5 Things Simultaneously: AI is slow. Solution? Run 4-5 AI sessions at once. While V0 builds, check email. While Codex compiles, answer Slack. "It's like having an army of assistants. I don't wait—I fill the dead time."9. The Dictatorship Advantage: Big companies need buy-in from PMs, designers, engineers, managers. Gumroad: Sahil → Devon → Production. "The hard part at big companies is aligning people to get behind a decision. It has nothing to do with actually shipping."10. Perfect the Business, Don't Scale It: $10M ARR, $7-8M EBITDA, $2M dividends last year, 1 employee, 35,000 creators. Goal: $10M EBITDA, then perfect the software. "I just want to work on software, make it better, have people use it, be proud of the work we do."----Where to Find Sahil Lavingia* Linkedin* X (Twitter)* Gumroad----Related ContentPodcasts:* We Built an AI Employee in 62 mins* Conversation with the CEO and Founder of Bolt* This $20M AI Founder Is Challenging Elon and Sam Altman | Roy Lee, CluelyNewsletters:* How to Build AI Products Right* Ultimate Guide to AI Prototyping Tools* The Fintech Super App Wars----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

Tyler Fisk built a $1.6 million AI education business in one year.Zero PhDs. Zero Silicon Valley pedigree. Just a systematic approach to building AI agents that actually work in production.While everyone’s vibe coding in ChatGPT, Tyler’s teaching thousands of students to build multi-agent systems for real businesses. Hundreds of production deployments. Actual revenue.Today he’s doing a live build: Taking Apple customer service from idea to working multi-agent system in under 90 minutes.No theory. Pure execution.----Brought to you by:* 1. Maven: Get $135 off Tyler’s course with my code AAKASHxMAVEN * Vanta: Get $1,000 off AI security & compliance at vanta.com/acos* Testkube:* Kameleoon: Leading AI experimentation platform* The AI Evals Course for PMs: Get $1155 off with code ‘ag-evals’----Key takeaways:1. Stop Vibe Coding: Most teams write one prompt, test twice, ship to production, and hope for the best. Tyler's rule: "We would never put it into production without a human-in-the-loop checkpoint. That's irresponsible." Start with 100% human review, gradually move to 60-70% autonomy.2. Use Meta-Prompting to Build Agents: Tyler built Gigawatt—an agent with 72,000 characters of system instructions that builds other agents. It researches the domain, writes V1 instructions, evaluates itself (scores out of 100), identifies gaps, and rewrites to V2. Goes from 77% to 86%+ quality.3. Build Multi-Agent Architectures: Don't build one agent that does everything. Separate concerns like you'd separate teams. For Apple: Core (expert agent, temp=0, finds facts) + Echo (email agent, temp=0.7, writes responses). Each optimized for its specific role.4. System Instructions Need 7K-9K Tokens: Structure includes Role (job description), Context (business details), Instructions (step-by-step process), Criteria (guardrails), Examples (meta reasoning). Most people write 200 tokens. Tyler writes 7,000-9,000. That's the foundation.5. Temperature Is Your Secret Weapon: Tyler's Toy Story analogy: Imagine an icy peak in a claw machine. Temp=0 (frozen): claw picks from top only—deterministic, precise. Temp=1 (melted): claw grabs anywhere—creative, varied. Match temperature to agent's job.6. Information Hierarchy Prevents Hallucinations: Priority order: RAG database first (scraped company docs), System instructions second (built-in expertise), Web search third (with chain-of-verification). When agents search without verification, they hallucinate.7. Build Complete Workflows: Tyler's 9-step production workflow with 5+ agents: Email arrives → Sentiment analysis (Cinnamon) → Expert research (Core) → Email writing (Echo) → QA loop → Human checkpoint (Slack) → Generative filter → Send → Log to memory.8. Observational Evals Come First: Test 20+ different scenarios manually. Include edge cases and adversarial inputs. Document every failure. Save golden examples. Only after building confidence do you add systematic evals in production.9. Calculate ROI as Labor Cost Reduction: Traditional cost: $460/day (expert time + customer service rep + manager review) = $138K/year. AI cost: $153/day (platform fees + API credits + human review) = $45.9K/year. Savings: $92K annual (67% reduction).10. Emotion Prompting Actually Works: Tyler ends every prompt with "Go get 'em slugger." Based on research: positive reinforcement improves LLM outputs by ~15%. The same psychology that works on humans works on LLMs. "Be nice to your AI. They're gonna have robot bodies soon."----Related ContentPodcasts:* Warp CEO on Profitable AI Agents* Elizabeth Laraki on AI Product Design* Claude Code TutorialNewsletters:* AI Agents: The Ultimate Guide for PMs* How to Build AI Products Right* Ultimate Guide to 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

Today’s EpisodeEveryone’s building AI products wrong.They’re sprinkling AI on top like fairy dust. Adding chat interfaces to everything. Ignoring 70 years of design principles.Elizabeth Laraki was one of 4 designers on Google Search in 2006. One of 2 designers on Google Maps in 2007. She helped create products used by billions—products whose designs barely changed for 15+ years because they nailed it from the start.Today she breaks down exactly how to design AI features that users actually love.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:* Vanta: Automate compliance, manage risk, and prove trust* Kameleoon: Leading AI experimentation platform* The AI PM Certificate: Get $550 off with ‘AAKASH550C7’* The AI Evals Course for PMs: Get $1155 off with code ‘ag-evals’----Timestamps:00:00:00 - Intro00:01:52 - Elizabeth's background at Google00:04:19 - Google's AI search integration00:06:19 - Designing image & video for AI00:09:44 - AI image expander disaster00:16:05 - Ads00:17:50 - AI safeguards & human-in-the-loop00:18:28 - 3-step AI design process00:31:29 - Ads00:33:25 - Designing AI voice interfaces00:38:25 - Designing beyond chat00:41:52 - AI design tools for designers00:44:49 - Live design: LinkedIn for AI00:57:04 - Google Maps redesign story01:04:14 - Google Maps India landmarks01:10:09 - Where to find Elizabeth01:12:00 - Outro----Key Takeaways1. The Core Design Process Hasn't Changed: Define the product (who, what tasks, what needs), Design it (features, architecture, flows), Build it (UIs, brand). Don't skip to "let's add a chatbot" because you have API access. The fundamentals still apply for AI.2. AI Adds Non-Deterministic Risk: Traditional software is deterministic - click A, get B every time. AI is non-deterministic with unpredictable outputs. Elizabeth's image expander added a bra strap that wasn't in the original photo. Completely unintentional, completely unacceptable.3. Work With Research on Safeguards: Audit training data for bias. Build evals that flag sensitive content (human bodies, faces, private information). Show A/B options for ambiguous cases. Make AI's work visible in the UI so users can scrutinize changes.4. Start With Jobs To Be Done: Don't ask "We have GPT-4, what should we build?" Ask "What painful workflow takes users hours?" Descript mapped video editing lifecycle and baked AI into each job: remove filler words, edit from transcript, create clips, write titles.5. Map User Context, Not Just Needs: ChatGPT voice in car with three kids? Perfect - nobody's looking at screen. Meta Ray-Bans reading Spanish menu item by item? Terrible - should ask "What are you in the mood for?" Same AI, different context requires different design.6. Emerge From Ambiguity First: For "LinkedIn for AI," Elizabeth mapped 4 possible directions, picked Matchmaking, identified AI's unlock (personality patterns vs keyword matching), mapped separate UIs for job seekers and employers. Only then touch pixels.7. Chat Fails for Complex Tasks: Elizabeth tried creating Madrid itinerary in ChatGPT. Every change regenerated everything with new hallucinations. Chat works for Q&A but fails for document creation, visual tasks, multi-step workflows that need persistent editable outputs.8. Make Chat Supporting, Not Primary: Photoshop embeds AI in existing canvas tools. Google Search shows AI summaries inline in normal results. Cove gives canvas with multiple AI conversations in parallel. Chat is a tool, not THE interface.9. Stop Adding AI Sprinkles: Elizabeth: "I can't help but think of this massive container of AI sprinkles everybody's shoving on top." Twitter/X + Grok, Amazon + Rufus, Apple Photos all feel forced. Ask three questions: Is this solving a real problem? Does chat make sense? Can you show your work?10. Google Maps India Innovation: Researched how Indians actually navigate (by landmarks, not street names). Identified which landmarks work (visible from street level like temples, petrol stations). Redesigned entire directions system around that insight. That's design, whether AI or not.----Where to Find Elizabeth Laraki* Linkedin* X (Twitter)----Related ContentPodcasts:What it means to be Design-LedComplete Tutorial to AI Prototyping5 AI Agents Every PM Should BuildNewsletters:Ultimate Guide to Product DesignUltimate Guide to AI PrototypingHow to Work With Design for Success----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

Today’s EpisodeClaude Code hit $500 million ARR in four months.Two product managers. Zero marketing dollars. Just pure viral growth.While some PMs are still copying and pasting into ChatGPT, others are orchestrating multiple AI agents that work in parallel, automatically reading files, researching competitors, and building prototypes.Carl Vellotti runs the world’s largest PM Instagram account (55K followers) and has mastered Claude Code better than almost anyone. He’s built his own meme generation system, automated his content workflow, and uses Claude Code for everything from research to prototyping.Today’s tutorial takes you from beginner to Claude Code hero.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:* Linear: Plan and build products like the best----Key Takeaways1/ Stop Working in Chat WindowsTraditional chat requires manually dragging files one at a time. Claude Code lives in your terminal and automatically reads entire folder structures. The interface was the bottleneck all along.2/ Build Your Knowledge Base FirstCreate four folders: business-info.md for product context, writing-styles/ for different voices, examples/ for past PRDs, meeting-transcripts/ for automatic uploads. One prompt pulls from everything.3/ Use the CLAUDE File for MemoryAdd rules once, they persist forever. "Never commit without asking." "Always use technical writing." Unlike prompts that get lost in context windows, this stays active every session.4/ Save Your Best Prompts as CommandsCreate /meeting-notes, /competitive-research, /prd-review. Save once, reuse forever. No more hunting through old Twitter bookmarks for that perfect prompt.5/ Let Claude Plan Before ExecutingPress Shift+Tab for Plan Mode. Claude creates full execution plan without touching files. You review, catch mistakes, then approve. This one habit prevents 80% of AI disasters.6/ Parallelize Everything You CanNeed to analyze 3 customer interviews? Claude spins up 3 UXR agents working simultaneously. Week of manual work becomes 1 hour with parallel agents.7/ Build Custom Agent PersonalitiesDesigner agent focuses on UX. Engineer agent checks technical constraints. Executive agent evaluates business impact. All three review your PRD simultaneously with specialized perspectives.8/ Use the $37/Month ComboClaude Pro ($17) handles research and writing perfectly. Add Cursor ($20) for heavy coding. You get best models for $37 instead of $200/month Claude Max.9/ Only See Token Usage HereClaude Code shows real-time token consumption and cost. Finally understand what API pricing actually means. No other interface gives you this visibility.10/ Start Simple Then ScaleBegin with one research task using file analysis. Add a custom command. Try parallel agents once. You'll never go back to chat interfaces.----Where to Find Carl Vellotti* Linkedin* X (Twitter)* Instagram----Related ContentPodcasts:Cursor TutorialWindsurf TutorialAI Prototyping TutorialNewsletters:AI Agents: The Ultimate Guide for PMsUltimate Guide to AI Prototyping ToolsHow to Land a $300K+ AI Product Manager Job----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

Today’s EpisodeAs an AI PM, you’re probably tired of building AI Agents and don’t know how to monetize them.But what if I told you there’s a company adding $1 million ARR every 10 days with their AI agent?Zach Lloyd, CEO of Warp and former Google engineering leader, cracked the code. His terminal-based AI agent has 700,000+ active developers paying real money.This episode is his complete playbook for AI PMs who want to build agents that actually make money.I hope you enjoy this one!----Brought to you by:* Vanta: Automate compliance, manage risk, and prove trust* Kameleoon: Leading AI experimentation platform* Amplitude: The market-leader in product analytics* The AI Evals Course for PMs: Get $1155 off with code ‘ag-evals’----Timestamps00:00:00 - Intro00:01:55 - Interview Begins00:02:02 - Warp's Scale & Growth00:03:08 - The Turning Point00:04:32 - Learn or Get Left Behind00:05:50 - Framework for AI Value00:08:30 - Warp's Development Process00:12:28 - UX Challenges in Agentic Products00:14:53 - Ads00:19:29 - Who's Making Money with Agents00:28:31 - Future Predictions00:29:24 - Ads00:30:26 - Contrarian Takes on AI's Future00:35:44 - 90-Day Roadmap for PMs00:38:33 - Outro----Key Takeaways----Where to Find Zach Lloyd* Linkedin* X (Twitter)* Warp----Related ContentPodcasts:* He built the top AI agent startup* AI Agents for PMs in 69 Minutes* How to Build AI Agents (and Get Paid $750K+)Newsletters:* AI Agents: The Ultimate Guide for PMs* Ultimate Guide to AI Prototyping Tools* How to Land a $300K+ AI Product Manager Job----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

Today's EpisodeHere's what's happening right now:Someone can clone your voice from a few YouTube videos and call your help desk pretending to be you.AI can build a perfect fake of your login page in minutes.This isn't some distant future threat. Jack Hirsch, VP of Product at Okta, sees this happening every day. Okta protects millions of logins and Jack has a front-row seat to how AI is completely changing cyber attacks.And the scary part is most PMs have no idea this is happening to their products.That's why I brought Jack on the show. He breaks down what's really happening and what you need to know as someone building products in the AI era.----Brought to you by:* Amplitude: The market-leader in product analytics* The AI Evals Course for PMs: Get $1155 off with code ‘ag-evals’* The AI PM Certificate: The #1 AI PM certificate* Kameleoon: Leading AI experimentation platform----Key Takeaways1. Identity is Everything: Over 80% of breaches stem from identity attacks, not device or network vulnerabilities. You cannot get security right without getting identity right - this is the new reality.2. DPRK Infiltration Operations: North Korean agents are passing full interview processes, getting hired, having laptops shipped to device farms, and operating as inside threats within major organizations.3. AI Agents = Security Blindspot: Companies deploy AI agents en masse without treating them as identities requiring access management. JP Morgan's CISO called this out as the biggest current threat vector.4. Help Desk Social Engineering: Attackers use AI voice cloning and deepfakes to impersonate employees calling help desk for password resets, MFA bypasses, and account access - often successfully.5. Session Security Over Time: Authentication degrades after login. Okta focuses on continuous session monitoring and risk signal sharing between security vendors rather than constant MFA prompts.6. T-Shaped Identity Strategy: Deep identity security (phishing-resistant auth, lifecycle management, risk sharing) plus broad integration across all enterprise systems - not just SSO and MFA.7. Cross-App Access Standard: New OAuth standard allows AI agents to inherit user permissions across enterprise apps without individual OAuth dances for thousands of employees.8. Essential vs Discretionary AI: Essential AI (bot detection, fraud prevention) stays always-on. Discretionary AI (log summaries, access reviews) gives customers opt-out control for compliance.9. AI Product Principles: Accelerate don't abdicate, solve real problems before prototyping, ignore AI hype cycle. Use AI as thought partner, not replacement for product judgment and domain expertise.10. Personal Security Stack: Lock credit reports immediately, use password manager with unique passwords, enable passkeys everywhere, lock phone number with carrier PIN to prevent SIM swapping attacks.----Related ContentPodcasts:How to Get a Product Leadership JobHow He Became a Series C VP of Product in 10 Years“Product Management isn’t going to exist in 5 years” - 2x CPONewsletters:The Product Leadership Job SearchThe Product Leader’s Ultimate Guide to Process ChangesProduct Leadership Interviews (GPM, Director, VP): How to Succeed----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

You use ChatGPT. But being an AI-powered PM means also using AI agents.In my slack poll, only 2% of you said you use AI agents for productivity. So I want to break that down and make it dead clear: 1) why you should use AI agents and 2) how you should build them.So in today’s episode, I’ve brought in Jacob Bank, former Director of PM at Google (Gmail, Calendar) and now CEO of the AI agent builder company Relay.app.He shares all his secrets - his 12 agent EA, his 40 agent marketing team, and his agent to synthesize agent updates. I hope you enjoy.----🏆 Thanks to our sponsors:Miro: The innovation workspace is your team's new canvasJira Product Discovery: Plan with purpose, ship with confidenceMobbin: Discover real-world design inspirationProduct Faculty: Product Strategy Certificate for Leaders (Get $550 off)----⏰ Timestamps:00:00 Intro01:49 Meet Jacob: The AI Agent Pioneer02:18 Managing Agent Notification Overload04:13 Current AI Agent Limitations Explained06:59 Relay's Growth & Bootstrap Strategy10:25 The Bull Case for AI Agent Market15:14 Ads17:18 Who's Adopting AI Agents Fastest20:46 Top 10 AI Agent Use Cases for PMs22:48 Choosing the Right Agent Platform28:44 Jacob's 55-Agent Marketing Team Breakdown31:47 Ads34:45 Building AI Agents Into Your Product38:10 MCP Protocol & Future of APIs41:43 Why Jacob Left Google Director Role44:25 Brutal Truth: PM-to-Founder Reality Check48:52 Outro----Key Takeaways1. Real agents need five components working togetherIntelligence (LLM), Knowledge (proprietary data), Memory (interaction history), Tools (APIs that change world state), Guardrails (validation and safety). Most "agents" are just LLM wrappers missing the other four components.2. No-code tools compress development cycles 100xLangflow + v0 enable 30-minute prototype-to-production workflows. Build competitive analysis agents live on screen. The cost barrier disappeared while customers still can't articulate what they want until they see it working.3. Cart-before-horse development beats traditional PM processSkip months of research. Build working prototypes first, test with real users, iterate based on feedback, then write focused PRDs. Speed beats perfection when technology moves this fast.4. FAANG salaries reflect desperate demandLevel 6-7: $750K+ total compensation. Level 8+: $1.2-1.5M total compensation. OpenAI: $900K+ for comparable roles. Growth rate: 2-3x faster than traditional PM positions because supply can't meet demand.5. The proven 18-month roadmap works systematicallyMonths 1-3: master fundamentals, build working agent solving personal problems. Months 4-9: scale to 10-20 real users, learn evaluation systems. Months 10-18: contribute to open source, prove you outperform existing team members.6. Vibe coding interviews test product judgment, not technical skillsDemonstrate structured thinking through prompt engineering, incorporate user insights in second iterations, show measurement frameworks in third iterations. They're evaluating product sense through AI interactions.7. Target problems with three characteristics for defensibilityDomain expertise you already possess, unstructured data requirements, complex decision-making processes. This combination creates competitive moats that simple AI features cannot replicate easily.8. Evaluation frameworks must come before codingMeasure usage adoption, outcome achievement, and user experience satisfaction. Include speed metrics (prompts to completion) and accuracy benchmarks (goal success rates) to validate that AI actually democratizes building.9. Company cultures reward different AI approachesMicrosoft: innovation without business constraints. Amazon: profit-focused execution speed. Meta: collaboration with world-class engineering talent. Google: user experience perfection with iteration time.10. Essential PM tools everyone needsCustomer interaction analyzer across all channels, AB testing simulator using AI personas at scale, document reviewer trained on your manager's specific feedback patterns an----Related ContentRelated Podcasts:* He built the top AI agent startup* AI Agents for PMs in 69 Minutes* How to Build AI Agents (and Get Paid $750K+)Realated Newsletters:* AI Agents: The Ultimate Guide for PMs* Ultimate Guide to AI Prototyping Tools* How to Land a $300K+ AI Product Manager Job----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

AI agent PM roles are the fastest-growing, highest-paid positions in tech. These jobs pay $750K+ (TC in SF/NY) and are growing 2-3x faster than traditional PM roles.But most people don't know how to actually build AI agents. They think it's just ChatGPT with a fancy interface.Today I sat down with Mahesh Yadav, who's worked as a PM at Meta, Amazon, Microsoft, and Google. He's built AI agents at scale for 8+ years and now teaches hundreds of PMs at top companies.He breaks down the exact playbook: how to build agents, the 18-month roadmap to $750K+ roles, and what FAANG companies look for in vibe coding interviews.If you want to learn to build AI agents, this is your blueprint.Check out the conversation on Apple, Spotify and YouTube.----Brought to you by:* Maven: Get $100 off my curation of their top courses with code ‘AAKASH550C7’* Miro: The innovation workspace is your team’s new canvas* Kameleoon: Leading AI experimentation platform* The AI Evals Course for PMs & Engineers: Get $1155 off with code ‘ag-evals’* Amplitude: The market-leader in product analytics----Timestamps00:00 - Introduction & Overview01:40 - What Makes an AI Agent PM02:37 - Building the Backend Agent16:32 - Creating the Frontend with V025:27 - What Defines an AI Agent vs AI Product30:15 - AI PM Interview Requirements34:08 - Cart Before the Horse Development37:15 - Breaking into FAANG: Mahesh's Story42:17 - Internal Transfer Strategy50:40 - Comparing Microsoft vs Amazon vs Meta vs Google54:28 - AI Agent PM Job Market & Salary Data57:26 - Can Anyone Become an AI PM?59:14 - 18-Month Roadmap to AI PM1:05:01 - AI Agents for Regular PMs1:08:47 - Business of Mahesh & Course Success----10 Steps to a $750K+ AI Agents Job:1. Build First (Not Study)The biggest mistake aspiring AI PMs make is spending months reading about AI instead of building. Companies like Google aren't looking for people who know frameworks—they want builders who have actually shipped AI products. Start with tools like Langflow for no-code backends and V0 for frontends.2. Master AI FundamentalsYou need to know how models work, how data contributes to these models, and how to evaluate agent performance. Can you make smart choices between different models? Do you understand how these models are built and how to interact with them? This knowledge separates real AI PMs from pretenders.3. Show Scale ExperienceFAANG companies desperately need people who have seen one major technology transition and navigated it successfully. Whether it was cloud migration, mobile, or something else, show you can handle the chaos that comes with emerging tech. They're looking for people who experiment constantly because AI is new for everyone.4. Prototype in WeeksThe cost of prototyping has dropped 100x in two years. Instead of spending six months on research and PRDs, build a working prototype in 2-3 weeks and show it to customers. This "cart before the horse" approach is now the competitive advantage in AI product development.5. Get 10-20 Real UsersFind a real problem you can solve—ideally one where you have PhD-level expertise, involves unstructured data, and requires complex decision-making. Build an agent to solve it and get at least 10-20 people actually using it. This teaches you evaluation and iteration in ways no course can.6. Scale to ProductionHire a small team of engineers (even remotely) and get your prototype into real production with 100+ users. This teaches you the difference between a demo and a scalable system. Many startups will let you do this for free in exchange for the experience and expertise you bring.7. Target Dream CompaniesPick your top 10 target companies and start contributing to their open communities. Run evaluations on their products for free. Show them gaps in their AI capabilities. Build features for their open-source models. Make yourself impossible to ignore by doing the work their PMs should be doing.8. Master Vibe CodingIn vibe coding interviews, they're not testing your technical skills—they're judging your product thinking. Show structured prompts, demonstrate how you iterate based on user feedback, and prove you can evaluate and improve AI systems. Practice the three-step framework: task, requirements, resources.9. Negotiate Multiple OffersAI PM roles at FAANG companies pay $750K-$1.5M+ total comp because demand far exceeds supply. Don't settle for one offer. The best candidates often get rejected by one company only to get double the salary elsewhere. Persistence pays—literally.10. Execute 18-Month TimelineMonth 1-3: Learn fundamentals and build your first agent. Month 4-6: Get 10-20 real users on a product you built. Month 7-12: Scale to production with 100+ users. Month 13-18: Contribute to target companies and interview. This timeline works because there's a level playing field in AI—your background matters less than your ability to ship.----Related Podcasts:* AI Agents for PMs in 69 Minutes* Full Roadmap: Become an AI PM* 5 AI Agents Every PM Should Build----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

Today's EpisodeRobinhood just crossed $100 billion in market cap. Its stock has 5.5x'd in the past year. It's one of the hottest companies in fintech.But here's what most people don't understand: building products at Robinhood isn't just about moving fast and breaking things. It's about moving fast while navigating regulations that could shut you down.Today I sat down with Abhishek Fatipurya, VP of Product at Robinhood, who's been there for 9 years - from intern to VP. He walked me through how they built products that democratized finance while staying compliant.If you're building in fintech or any regulated industry, this is your playbook.----⏰ Timestamps:00:00 Intro01:34 Robinhood's AI Assistant: Cortex08:01 Advice for Products in Fintech12:10 IPO Stories14:37 Ads16:31 How To Build Innovative Products21:30 Why Most Fintech PMs Fail at Experimentation27:15 Ads28:54 Training the Team30:48 Abhiskek Journey at Robinhood39:40 Layoffs47:02 Robinhood's Scaling Journey (2016-2025)52:54 Should Prototypes Replace PRD's1:05:40 Why most Fintech PMs are Failing1:10:48 How To Build a Real Product1:18:08 Outro----Brought to you by:1. Kameleoon: Leading AI experimentation platform - kameleoon.com/prompt2. Mobbin: Discover real-world design inspiration - https://mobbin.com/?via=aakash3. AI Evals Course for PMs & Engineers: Get $1155 off with code ag-evals - https://maven.com/parlance-labs/evals?promoCode=ag-evlas4. Amplitude: The market-leader in product analytics - https://amplitude.com/session-replay?utm_campaign=session-replay-launch-2025&utm_source=linkedin&utm_medium=organic-social&utm_content=productgrowthpodcast----Key Takeaways1. Build AI products around problems customers already have rather than creating AI for AI's sake - Robinhood identified core pain points like "why did this stock move?" then built solutions that fit existing workflows instead of forcing new behaviors.2. Write your product's "swipeys" (onboarding screens) before building anything to force clarity on value proposition. If you can't convince a customer to hit "get started" in one sentence on mobile, you don't have a great product.3. Curate upstream data sources and focus on information rather than recommendations when building AI for regulated industries. Robinhood secures licenses with news providers while carefully prompting AI to avoid investment recommendations that trigger regulatory issues.4. Transform legal teams into product partners by hiring domain experts who get excited about building great customer experiences within regulatory constraints. Former SEC regulators who understand both rules and product vision push for better solutions rather than adding friction.5. Obsess over pixel-perfect details because great design shouldn't be reserved for high-net-worth customers in financial services. When the CEO spends time on animation details, it creates a competitive moat where most companies use bad design as barriers.6. Test everything relentlessly instead of copying surface tactics - Robinhood's referral program went through 60+ iterations, evolving from $10 cash to variable stocks. Most fintechs copy "$20 for $20" without understanding the deeper insight: give users your core service, not generic rewards.7. Democratize access by speaking to customer pain points rather than industry jargon. "Get in at the IPO price" addressed frustration of watching stocks gap up from $20 to $50 on opening day, making access emotionally resonant.8. Unite cross-functional teams under shared business goals by switching from functional silos to business unit GMs. This eliminates "death by a thousand departments" where each function adds friction without considering holistic customer experience.9. Think mobile-first to force clearer communication and simpler flows since mobile constraints eliminate unnecessary complexity. Even internal planning revolves around what features will be showcased in mobile-centric product keynotes.10. Ship meaningful features consistently to create a virtuous cycle where teams stay focused and the market recognizes you as an innovation engine. This product velocity compounds into sustained performance by demonstrating consistent execution capability.----Related ContentPodcasts:AI Product Leadership with Julie ZhuoAI Experimentation with Fred de TodaroAI Product Discovery with Teresa TorresNewsletters:Should you invest in your referrals channel?How to Build AI Products RightThe Fintech Super App Wars----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

Today's EpisodeWhat makes AI agents different from chatbots?That’s the question we break down from every angle with today’s guest.Armand Ruiz, VP of AI Platform at IBM, who has been in AI for 16 years and has become one of the most-followed AI voices on LinkedIn.Armand leads AI platforms at IBM, building the building blocks for enterprises to build AI agents securely. He spends his time meeting with CIOs from the biggest brands who all have AI as their number one priority - and agents as one of their core components.In our conversation, he breaks down:* How AI agents differ from the chatbots we know* The four-step framework every agent needs* Why RAG systems power 90% of enterprise AI* How product management changes when agents do the work----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:* Kameleoon: AI experimentation.* The AI Evals Course for PMs & Engineers: You get $800 with this link* Vanta: Automate compliance, manage risk, and prove trust* Amplitude: Try their 2-min assessment of your company’s digital maturity* Product Faculty: Product Strategy Certificate for Leaders (Get $550 off)----Timestamps00:00 Intro02:39 What Makes AI Agents Special04:40 The Four Steps of AI Agents07:14 AI Agent Development Frameworks12:59 RAG Explained16:55 ADS18:46 Common RAG Mistakes26:48 Managing Multiple AI Agents31:39 ADS33:57 How AI Changes Product Management37:43 Problem Investigation vs Feature Factory41:22 Roadmap to Build AI Agents43:30 Can Open Source AI Win?51:39 IBM's AI Strategy59:32 Career Journey: Intern to VP1:02:36 Building 200K LinkedIn Followers1:08:18 Outro----Key Takeaways1. AI Agents vs Chatbots: Chatbots respond to queries while agents execute complete workflows. The difference between getting suggestions and getting finished work.2. Four-Step Agent Framework: Every agent needs Thinking (reasoning), Planning (task breakdown), Action (system execution), and Reflection (learning from outcomes).3. RAG Dominates Enterprise: 90% of enterprise AI uses RAG to connect LLMs to proprietary data. Success requires 95%+ accuracy through sophisticated evaluation.4. Vision RAG Unlocks Value: Most business data lives in charts and tables that traditional text-only RAG completely misses.5. Framework Selection Matters: Use coding frameworks (LangGraph, CrewAI) for complex systems. Use no-code tools (Lindy, n8n) for rapid prototyping.6. PM Ratios Transform: Traditional 1:6-10 PM-to-developer ratios become 1:2-30 when agents handle research and documentation.7. Prototypes Beat PRDs: Show working systems instead of 20-page documents teams misinterpret. AI enables functional demos.8. Open Source Wins: Despite closed-source capabilities, enterprises choose open source for licensing control and infrastructure flexibility.9. Technical Literacy Essential: Understanding agents, RAG, and frameworks becomes baseline knowledge for everyone, not just developers.10. Implementation Reality: Enterprise RAG needs heavy data engineering. Teams underestimate accuracy requirements and engineering complexity.----Related ContentPodcasts:We Built an AI Agent to Automate PM in 73 minsWe Built an AI Employee in 62 mins5 AI Agents Every PM Should BuildNewsletters:AI Agents: The Ultimate Guide for PMsAI Evals for AgentsStep-by-Step RAG----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

Today's guest: Julie Zhuo, Former VP of Product Design at Facebook, Wall Street Journal bestselling author of "The Making of a Manager," and now AI product leader at Sundial"Is the product designer role going to exist in 10 years? Is the product manager role going to exist in 10 years?"That's Julie Zhuo asking the existential questions every product leader is thinking but afraid to voice.Julie spent 13 years at Facebook, starting as an IC designer and rising to VP of Product Design. She wrote the Wall Street Journal bestseller "The Making of a Manager." Today, she's building AI products at Sundial and working with companies like OpenAI.In our conversation, she breaks down:* How AI is killing traditional product roles* The timeless management principles that still matter* What makes a great AI product leader* How to build product taste when AI gets better than youThis isn't just about adapting to new tools. It's about reimagining what product development looks like when one person can do what used to take a whole team.----Check out the conversation on Apple, Spotify and YouTube.Mobbin: Discover real-world design inspirationJira Product Discovery: Build the right thing, reliablyProduct Faculty: Product Strategic Certificate for Leaders (Get $550 off)The AI Evals Course for PMs & Engineers: You get $800 with this link----Timestamps00:00 Intro02:30 The Death of Product Development08:42 Learn The Craft15:02 ADS17:00 Definition of a Managers's Job21:12 Julie's Thoughts on AI Agents28:12 Blindspots While switching from IC to Manger30:40 ADS35:48 The Three Levers That Never Change41:20 What is Feedback46:43 How AI is Changing the Domain52:49 What Makes Great AI Product Leaders Different1:00:55 Essential AI Tools Every Leader Should Master1:09:15 Lessons from OpenAI's Product Team1:15:55 Outro----Key Takeaways1. Stop Thinking in Roles, Start Thinking Skills. The future belongs to builders who combine unique strengths with AI capabilities, not people attached to traditional job titles like PM or designer.2. Taste Becomes the Critical Differentiator. When AI can do many things well, your ability to recognize exceptional work versus average output becomes your most valuable skill.3. The Three Management Levers Still Apply. People, process, and purpose remain the core levers. AI agents just add new tools within the "people" lever you need to manage.4. Face Reality to Build Trust. Create environments where teams can confront what's really happening. Thank messengers who bring problems instead of shooting them.5. Conviction + Humility Balance. Have strong conviction in your process and vision, but stay humble enough to accept feedback and iterate based on what you learn.6. Be a Beginner Again. Even experienced product leaders need to earn their stripes in the AI era. The willingness to learn matters more than past success.7. Lead Through Experimentation. This isn't a playbook era. Try new team structures, new workflows, new approaches. Nobody has all the answers yet.8. Master AI Tools in Your Workflow. Don't just use ChatGPT occasionally. Actively disrupt your old systems and use AI throughout your daily work processes.9. Learn from OpenAI's Approach. They work seven days a week, obsess over understanding user behavior data, and maintain rigorous weekly metrics reviews for alignment.10. Focus on What Remains Human. The joy of creation, learning processes, and meaning we derive from building things we're proud of can't be automated away.----Related ContentPodcasts:Full Roadmap: Become an AI PMComplete Course: AI Product ManagementHow to Become, and Succeed as, an AI PM | The Marily Nika EpisodeNewsletters:How to become an AI Product ManagerHow to Write a Killer AI Product Manager ResumeHow to Become an AI Product Manager with No Experience----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