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. 5 दिन पहले

    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 घं॰ 12 मि॰

  2. 4 अक्टू॰

    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 घं॰ 37 मि॰

  3. 27 सित॰

    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 मिनट

  4. 22 सित॰

    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 घं॰ 30 मि॰

  5. 19 सित॰

    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 मिनट

  6. 13 सित॰

    FAANG PM Reveals How to Build AI Agents (and Get Paid $750K+)

    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 ---- Timestamps 00:00 - Introduction & Overview 01:40 - What Makes an AI Agent PM 02:37 - Building the Backend Agent 16:32 - Creating the Frontend with V0 25:27 - What Defines an AI Agent vs AI Product 30:15 - AI PM Interview Requirements 34:08 - Cart Before the Horse Development 37:15 - Breaking into FAANG: Mahesh's Story 42:17 - Internal Transfer Strategy 50:40 - Comparing Microsoft vs Amazon vs Meta vs Google 54:28 - AI Agent PM Job Market & Salary Data 57:26 - Can Anyone Become an AI PM? 59:14 - 18-Month Roadmap to AI PM 1:05:01 - AI Agents for Regular PMs 1:08:47 - 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 Fundamentals You 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 Experience FAANG 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 Weeks The 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 Users Find 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 Production Hire 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 Companies Pick 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 Coding In 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 Offers AI 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 Timeline Month 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

    1 घं॰ 12 मि॰

  7. 11 सित॰

    How to Build AI products in FinTech | $100B Lessons from Robinhood VP PM

    Today's Episode Robinhood 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 Intro 01:34 Robinhood's AI Assistant: Cortex 08:01 Advice for Products in Fintech 12:10 IPO Stories 14:37 Ads 16:31 How To Build Innovative Products 21:30 Why Most Fintech PMs Fail at Experimentation 27:15 Ads 28:54 Training the Team 30:48 Abhiskek Journey at Robinhood 39:40 Layoffs 47:02 Robinhood's Scaling Journey (2016-2025) 52:54 Should Prototypes Replace PRD's 1:05:40 Why most Fintech PMs are Failing 1:10:48 How To Build a Real Product 1:18:08 Outro ---- Brought to you by: 1. Kameleoon: Leading AI experimentation platform - kameleoon.com/prompt 2. Mobbin: Discover real-world design inspiration - https://mobbin.com/?via=aakash 3. AI Evals Course for PMs & Engineers: Get $1155 off with code ag-evals - https://maven.com/parlance-labs/evals?promoCode=ag-evlas 4. 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 Takeaways 1. 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 Content Podcasts: AI Product Leadership with Julie Zhuo AI Experimentation with Fred de Todaro AI Product Discovery with Teresa Torres Newsletters: Should you invest in your referrals channel? How to Build AI Products Right The 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

    1 घं॰ 19 मि॰

  8. 5 सित॰

    AI Agents for PMs in 69 Minutes — Masterclass with IBM VP

    Today's Episode What 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) ---- Timestamps 00:00 Intro 02:39 What Makes AI Agents Special 04:40 The Four Steps of AI Agents 07:14 AI Agent Development Frameworks 12:59 RAG Explained 16:55 ADS 18:46 Common RAG Mistakes 26:48 Managing Multiple AI Agents 31:39 ADS 33:57 How AI Changes Product Management 37:43 Problem Investigation vs Feature Factory 41:22 Roadmap to Build AI Agents 43:30 Can Open Source AI Win? 51:39 IBM's AI Strategy 59:32 Career Journey: Intern to VP 1:02:36 Building 200K LinkedIn Followers 1:08:18 Outro---- Key Takeaways 1. 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 Content Podcasts: We Built an AI Agent to Automate PM in 73 mins We Built an AI Employee in 62 mins 5 AI Agents Every PM Should Build Newsletters: AI Agents: The Ultimate Guide for PMs AI Evals for Agents Step-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

    1 घं॰ 9 मि॰
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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

जानकारी

  • निर्माता
    Aakash Gupta
  • सक्रिय वर्ष
    2024 - 2025
  • एपिसोड
    102
  • रेटिंग
    श्लील
  • कॉपीराइट
    © Aakash Gupta
  • कार्यक्रम की वेबसाइट
    Product Growth Podcast

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