Product Growth Podcast

Aakash Gupta

The latest insights into how great products grow, how to be a better PM or product leader, and how to get a PM job. www.news.aakashg.com

  1. 1小时前

    Google AI PM Director drops an AI PM Masterclass + Tutorial on Google's AI Tools

    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 insider How to build great AI products How to become an AI PM And 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 Takeaways 1. 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 Content Podcasts: * 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 Management Newsletters: * 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

    32 分钟
  2. 3天前

    How to PM Production Changes with Devin: Tutorial From Gumroad CEO, Sahil Lavingia

    Today’s Episode Sam 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 Takeaways 1. 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 Content Podcasts: * 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, Cluely Newsletters: * 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

    28 分钟
  3. 6天前

    How to Build Multi-Agent AI Systems That Actually Work in Production | Tyler Fisk

    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: * Maven: Get $300 off Mahesh’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 Content Podcasts: * Warp CEO on Profitable AI Agents * Elizabeth Laraki on AI Product Design * Claude Code Tutorial Newsletters: * 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

    1 小时 41 分钟
  4. 10月7日

    Crash Course in AI Product Design from Google Search + Maps Designer, Elizabeth Laraki

    Today’s Episode Everyone’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 - Intro 00:01:52 - Elizabeth's background at Google 00:04:19 - Google's AI search integration 00:06:19 - Designing image & video for AI 00:09:44 - AI image expander disaster 00:16:05 - Ads 00:17:50 - AI safeguards & human-in-the-loop 00:18:28 - 3-step AI design process 00:31:29 - Ads 00:33:25 - Designing AI voice interfaces 00:38:25 - Designing beyond chat 00:41:52 - AI design tools for designers 00:44:49 - Live design: LinkedIn for AI 00:57:04 - Google Maps redesign story 01:04:14 - Google Maps India landmarks 01:10:09 - Where to find Elizabeth 01:12:00 - Outro ---- Key Takeaways 1. 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 Content Podcasts: What it means to be Design-Led Complete Tutorial to AI Prototyping 5 AI Agents Every PM Should Build Newsletters: Ultimate Guide to Product Design Ultimate Guide to AI Prototyping How 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

    1 小时 12 分钟
  5. 10月4日

    The Claude Code Tutorial for AI PMs: Why You Need to Use It + How

    Today’s Episode Claude 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 Takeaways 1/ Stop Working in Chat Windows Traditional 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 First Create 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 Memory Add 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 Commands Create /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 Executing Press 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 Can Need 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 Personalities Designer 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 Combo Claude 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 Here Claude 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 Scale Begin 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 Content Podcasts: Cursor Tutorial Windsurf Tutorial AI Prototyping Tutorial 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

    1 小时 37 分钟
  6. 9月27日

    The AI PM’s Guide to Building AI Agents, with Warp CEO Zach Lloyd

    Today’s Episode As 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 - Intro 00:01:55 - Interview Begins 00:02:02 - Warp's Scale & Growth 00:03:08 - The Turning Point 00:04:32 - Learn or Get Left Behind 00:05:50 - Framework for AI Value 00:08:30 - Warp's Development Process 00:12:28 - UX Challenges in Agentic Products 00:14:53 - Ads 00:19:29 - Who's Making Money with Agents 00:28:31 - Future Predictions 00:29:24 - Ads 00:30:26 - Contrarian Takes on AI's Future 00:35:44 - 90-Day Roadmap for PMs 00:38:33 - Outro ---- Key Takeaways ---- Where to Find Zach Lloyd * Linkedin * X (Twitter) * Warp ---- Related Content Podcasts: * 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

    39 分钟
  7. 9月22日

    The AI PM's Guide to Security - with Okta's VP of PM & AI, Jack Hirsch

    Today's Episode Here'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 Takeaways 1. 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 Content Podcasts: How to Get a Product Leadership Job How He Became a Series C VP of Product in 10 Years “Product Management isn’t going to exist in 5 years” - 2x CPO Newsletters: The Product Leadership Job Search The Product Leader’s Ultimate Guide to Process Changes Product 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

    1 小时 30 分钟
  8. 9月19日

    How to Build AI Agents to 10x your PM Productivity with CEO of Relay.app (fmr Dir PM of Gmail)

    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 canvas Jira Product Discovery: Plan with purpose, ship with confidence Mobbin: Discover real-world design inspiration Product Faculty: Product Strategy Certificate for Leaders (Get $550 off) ---- ⏰ Timestamps: 00:00 Intro 01:49 Meet Jacob: The AI Agent Pioneer 02:18 Managing Agent Notification Overload 04:13 Current AI Agent Limitations Explained 06:59 Relay's Growth & Bootstrap Strategy 10:25 The Bull Case for AI Agent Market 15:14 Ads 17:18 Who's Adopting AI Agents Fastest 20:46 Top 10 AI Agent Use Cases for PMs 22:48 Choosing the Right Agent Platform 28:44 Jacob's 55-Agent Marketing Team Breakdown 31:47 Ads 34:45 Building AI Agents Into Your Product 38:10 MCP Protocol & Future of APIs 41:43 Why Jacob Left Google Director Role 44:25 Brutal Truth: PM-to-Founder Reality Check 48:52 Outro ---- Key Takeaways 1. Real agents need five components working together Intelligence (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 100x Langflow + 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 process Skip 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 demand Level 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 systematically Months 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 skills Demonstrate 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 defensibility Domain 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 coding Measure 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 approaches Microsoft: 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 needs Customer 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 Content Related 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

    49 分钟
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The latest insights into how great products grow, how to be a better PM or product leader, and how to get a PM job. www.news.aakashg.com

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