The Growth Podcast

Check out the conversation on Apple, Spotify, and YouTube.Brought to you by:* Maven - Get a $675 discount off Gabor’s course with my code* Amplitude - The market-leader in product analytics* Testkube - The leading test orchestration platform* Land PM Job - My 12-week AI PM + Job Search Course starts Monday!* Product Faculty - Get $550 off their #1 AI PM Certification with code AAKASH550C7Today’s episodeHere’s the problem with most Claude Cost demos: they stop at the prototype.Nobody shows what happens next. You try to add a second feature. The first one breaks. The styling reverts to default. The code is so tangled that you spend more time debugging than you saved by generating.Gabor Mayer showed me what happens when you stop treating Claude Code like a magic prompt box and start treating it like a team.He is a PM at Google. He has not written production code in 15 years. But over the past several months, he has been building real mobile apps using 21 specialized Claude Code agents. Not prototypes that live in a demo. Apps that are on the App Store.In today’s episode, he walked through the entire workflow live and share all the resources free.If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.Do you want to become an AI PM? I’ve created a course for you. Starts next week.Newsletter deep diveThank you for having me in your inbox. Here is the complete guide to building a full AI development team in Claude Code:* Why one-prompt vibe coding fails* The 21-agent team architecture* The spec-first workflow * From design to code without touching either* What changes when PMs actually buildSave this. The full 10-step playbook on one page. Everything below is the why and how behind each step. 1. Why one-prompt vibe coding failsEvery PM I know has built something with Bolt, Lovable, or Replit. The prototype looks great. It runs. It impresses people in a Slack message.Then you try to ship it to real users. And you hit a wall.Blocker 1 - Context compression silently destroys your specThis is the failure mode that nobody talks about in tutorials. When you give one agent one massive prompt, the model compresses context. Details get dropped. Not randomly. Strategically. The model decides what is “important” and what is not.In the episode, Gabor defined a complete color palette. Oranges, neutrals, specific accent tones. The agent received everything. The output used none of it. The layout was there. The structure was solid. But every color was a default.The reason is straightforward. When the context window is full, visual styling details are lower priority than functional logic. So the model drops them. Silently. Without warning. Without an error message. You just get generic output and wonder what went wrong.The fix is not better prompts. It is context engineering. Smaller, scoped tasks. Each agent gets only the context it needs for its specific job. The designer agent gets the brand guideline. The CTO agent gets the architecture spec. Neither gets the full 50-page document.Blocker 2 - AI-generated code compiles but is not maintainableA Reddit comment that hit home for Gabor - “Vibe coding is just the rebranding of unmaintainable, low-quality source code.”This is the real prototype-to-production gap. The code works today. You can demo it. You can push it to TestFlight. But the moment you touch it to add a feature, three other features break. No naming conventions. Circular references between modules. Zero comments explaining why anything was built the way it was.The fix is a dedicated code quality agent. Gabor calls his the Spaghetti Agent. It runs after every sprint and checks naming conventions, circular references, comment coverage, and structural debt. When he ran it on his codebase for the first time, it caught issues he never would have found manually.If you are building anything beyond a one-off demo, this agent is not optional. I covered similar quality patterns in my AI testing guide and my AI evals deep dive.Blocker 3 - No dependency mapping means cascading failuresWhen you build without organizing work into sprints, agents try to build features that depend on code that does not exist yet. Front-end components reference API endpoints that have not been created. Database queries call tables that have not been defined.The Atlassian MCP currently cannot create sprints directly in JIRA. That is a real limitation. Gabor uses tags as a workaround. He tags tickets as Sprint 1, Sprint 2, Sprint 3 and maps dependencies between them manually before starting the build. Without this step, the entire multi-agent workflow falls apart.Every PM who has gone from prototype to production with AI agents has hit at least one of these blockers. The ones who shipped figured out the workarounds. The ones who quit assumed the tools were the problem.Here is what the three blockers look like side by side, and what flips the moment you stop one-prompting and start running a team.2. The 21-agent team architectureYou do not need 21 agents to start. Three will get you surprisingly far. But understanding the full architecture shows you where the complexity lives and which roles to add as your projects grow.Here is the full roster: four clusters, 21 roles, and the markdown file pattern that makes them portable across every project you build next.2a. The core agents every PM needsThe System Analyst is the linchpin. It breaks down product requirements into technical specifications. It asks clarifying questions one at a time. It documents decisions in Confluence. It creates tickets in JIRA. Without this agent, every other agent operates on incomplete context.In the episode, the system analyst asked 14 clarifying questions before a single line of documentation was written. Vector DB choice. Usage limit mechanics. Conversation history handling. Search fallback strategy. API provider. Minimum iOS version. Screen count. Naming conventions. Each question one at a time so the answers stay deep.The prompt pattern that makes this work -“Please act like a good system analyst. Ask clarifying questions until you have a complete and comprehensive understanding. Ask questions one at a time. Do not start writing documentation until all questions are answered.”Two critical instructions. “One at a time” prevents the agent from dumping 25 questions at once. “Do not start writing” stops it from jumping ahead before the spec is complete. Different LLMs have different tendencies. Some love to start coding instantly. You need to explicitly constrain them. This is the same principle behind the prompt engineering techniques that work across any AI tool.The Spaghetti Agent handles code maintainability. Naming conventions. Circular references. Comment quality. Structural debt. Born from that Reddit comment. When Gabor ran it on his codebase for the first time, it caught problems he never knew existed.The UX Flow Architect creates clickable prototypes using Figma’s built-in prototyping arrows. This is a small but important detail. The early versions of this agent placed visual drawn arrows between screens instead of using Figma’s actual prototyping connections. The prototype looked like it had navigation. But when you clicked play, nothing happened. It took months of iteration to fix.Each agent has a specific Claude Code agent markdown file that defines its role, its constraints, and its interaction patterns. The setup mirrors how you would build a Claude Code Team OS for a human team.2b. The real blockers nobody warns you aboutThe Figma MCP color problem. When you connect Claude Code to Figma through the MCP and pass it your full specification, the screens look structurally correct but the colors are wrong. Not slightly wrong. Completely wrong. The model compressed the context and dropped your entire visual identity. The fix is to pass the brand guideline as a separate, focused input to the Designer Agent. Never bundle it with the functional spec.The Atlassian MCP sprint limitation. The MCP currently cannot create sprints directly in JIRA. Gabor uses tags as a workaround. Sprint 1, Sprint 2, Sprint 3. It works. But it means dependency mapping is a manual step in the system analyst prompt, not an automated feature.The consumer app vs Claude Code gap. An agent role you set up in the Claude consumer app does not automatically transfer to Claude Code. You need to define agents separately in both environments. The system analyst in your consumer app conversation is a different instance from the system analyst in your Claude Code agent folder. Your AI PM stack needs to account for this separation.The $200 Max plan economics. On the Max plan, a major build session uses roughly 10% of your monthly allocation. That means you get about 10 full build sessions per month. For a side project, that is plenty. For a production workflow with daily iterations, you need to be deliberate about when you run multi-agent sprints.2c. Why reusable agents beat fresh setupsEvery painful lesson, every edge case fix, every API workaround gets encoded into the agent markdown file. The next project starts from a position of strength. The Spaghetti Agent that took weeks to calibrate on project one is immediately useful on project two. The UX Flow Architect that took months to stop drawing fake arrows works correctly from day one on every subsequent project.This is the compound interest of building with agents. The first project is slow. The second is faster. By the fifth, your agent team is genuinely effective. Gabor’s Maven course walks through the full setup at maven.com/gabor/productbuilder.The 21 agents are not the point. The point is that every role on a software team can be replicated by a scoped, reusable AI agent. Start with three. Add roles when you hit friction.3. The spec-first workflowMost tutorials start with the terminal. Open Claude Code. Start prompting. Start coding.That is backwards. The workflow that actually ships production apps starts in the consumer app. On your phone. Possibly while walking your dog. The process maps cleanly to the PM OS framework that works for any complex project.3a. Define the system analyst role firstBefore you describe your app, you ask the LLM to define what a good system analyst does. This creates a behavioral framework that the agent will follow for the rest of the conversation.The prompt -“What is the difference between a good system analyst and a bad system analyst in a software development team? Be as detailed as possible.”The response gives you a blueprint. Requirement elicitation. Stakeholder management. Process modeling. Dependency documentation. You then instruct the agent to act like a good system analyst.This is the same principle behind AI agents for PMs. Define the role explicitly before assigning the task. It works in Claude Cowork the same way it works in Claude Code.3b. Dictate, do not typeThis is where superwhisper changes the game. In the episode, the app specification was dictated in a single long monologue. Technology stack (Flutter + Firebase). Data storage rules (device-only, no server-side user data). API key security (Firebase Secret Manager, never exposed to front-end). Usage limits (20,000 word cumulative cap with escalating cooldowns). Tone of voice (friendly but firm, like a 20-year referee friend). Vector database configuration (Vertex AI embeddings for IIHF rulebook and Situation Book).Typing that specification would have taken 30 minutes and produced half the detail. Dictating it took five minutes and captured every nuance. The longest dictation prompt in the history of this podcast.Here is the actual prompt, the five-step workflow it kicks off, and the two-word constraint - “one at a time” - that stops the agent from face-planting. The key rule - even if you ramble, even if you are not perfectly concise, the LLM will understand. You lose nothing by over-specifying. You lose everything by under-specifying. This applies whether you are building a prototype or shipping to production.3c. Documentation before designThe system analyst creates the full Confluence documentation before any design or code begins. Product overview. Technical architecture. AI agent specification. Data flow diagrams. API endpoint mapping.Without documentation, every agent operates on partial context. With documentation, every agent operates on the same source of truth. I covered this exact approach in my PRDs guide. The principle is identical whether your team is human or AI.The boring part of building is the specification. The exciting part is watching agents create screens and write code. But if you skip the boring part, the exciting part produces garbage. The PMs who understand product strategy already know this.4. From design to code without touching eitherOnce the specification is locked, the workflow shifts from the consumer app to three parallel tracks. This is where the 21-agent architecture pays off and where most of the real-world friction surfaces.Three tracks - design, tickets, build - running in parallel into four sprints. 72 minutes from idea to App Store submission. Here is the map. 4a. Design through Figma Make and Claude CodeStart in Figma Make. Go to Spotted in Prod. Take screenshots of apps you admire. Feed those into Figma Make to create a brand guideline. Typography. Color palettes. CTA buttons. Error states. Transitions.In the episode, two inspiration images produced a full brand guideline. One of them was a photo of a laptop cover. Figma Make derived custom colors from the image without manual hex entry.Claude Code then used the Figma MCP to build actual screens in Figma based on that style guide. Five screens appeared in real time. Each one matching the brand guideline. The Chrome DevTool MCP lets Claude Code visually verify designs in a browser, catching visual bugs the Figma MCP alone cannot detect.4b. Tickets with the full team reviewThe system analyst creates JIRA tickets. The entire agent team reviews every ticket before development starts. This is the step that separates production builds from demo builds. Same product launch discipline, different toolchain.Designer agent verifies screenshots are attached. Test Architect ensures test coverage. Spaghetti Agent sets naming expectations. Product Council confirms data storage policies. CTO Agent validates architecture. This maps to the AI observability principles I wrote about previously.4c. Sprint execution with the dependency mapping workaroundTickets organized into sprints using tags (Atlassian MCP workaround). Dependencies mapped. Database setup in Sprint 1. API in Sprint 2. Front-end in Sprint 3. Integration in Sprint 4.“Claude, start building. Go for Sprint 1. Once done, Sprint 2, then Sprint 3, and so on. If you have any questions, ask.”Multiple agents work in parallel. The coding phase is the fastest part. On the $200 Max plan, roughly 10% per session.Everything before the code is the hard part. Once those are right, the code practically writes itself. This is true whether you are shipping to production as a PM or managing an engineering team.5. What PMs gain by building with agentsIf agents can spec, design, code, and test, what is the PM actually doing?Making product decisions. The tools just got absurdly faster.Gain 1 - Firsthand understanding of agent behaviorWhen you interact with agents daily, you develop intuition for context window limits, hallucination patterns, and compression behaviors. That intuition directly improves your roadmap decisions. You stop over-scoping agent features because you know where agents break down. You stop under-investing in evals because you have seen what happens without them.Gabor has not written production code in 15 years. But he now understands agent behavior better than most PMs who have only read about it. That understanding compounds across every product decision.Gain 2 - A portfolio that proves competenceA working app on the App Store is undeniable proof. Password-protect a section showing the build process. Confluence docs. JIRA tickets. Agent architecture. That portfolio item says more than any certificate. It says you shipped.Gain 3 - Iteration speed that compoundsThe first build is the hard part. The UX Flow Architect alone took months. The Spaghetti Agent needed weeks of tuning.But once v1 ships, everything accelerates. New features take a morning. The reusable agent files carry forward every lesson. The PM who has shipped one app can ship the next in a fraction of the time. Not because the tools are better. Because their agents are better.Stack those three gains over a year and the gap between PMs who build and PMs who watch stops being a gap. It becomes a moat.You do not need to know how to code. You need a willingness to understand how software works and the patience to specify before you build. If you want to get started, my Claude Code guide walks through the full setup.Where to find Gabor Mayer* Maven course - Go from PM to AI Builder* LinkedIn* XRelated contentPodcasts:* My Claude Code PM OS with Dave Killeen* Claude Code OS Layer with Carl Vellotti* How to Design like OpenAI and Figma with Ed Bayes and Gui SeizNewsletters:* The complete guide to Claude Code* AI agents for PMs* How to build AI productsPS. Please subscribe on YouTube and follow on Apple & Spotify. It helps! 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 episodeLinkedIn just changed the title of its product managers to product builders.What does it even mean to be a “builder PM”?Well, tools only get you so far. Learning Claude Code is helpful, but means nothing if you don’t have an understanding of the underlying first principles.That’s today’s episode.Mahesh Yadav created one of our most popular episodes, with over 35K views on YouTube, and now he’s back. Earlier, he taught you AI agents. Today, he’s touching you how to become a builder PM:If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.I’m giving a free talk on how to get interviews at the top AI PM companies on Thursday, April 23rd 2026 @ 9:00AM PDT. Grab your seat.----Check out the conversation on Apple, Spotify, and YouTube.Brought to you by:* Maven - Build cohort-based courses that scale* Amplitude - The market leader in product analytics* Jira Product Discovery - Prioritize what matters with confidence* NayaOne - Airgapped cloud-agnostic sandbox to validate AI tools faster* Product Faculty - Get $550 off their #1 AI PM Certification with my link----Key Takeaways:1. Builder PM defined - A builder PM talks to customers, figures out what to build, and ships the first version to 10 customers without talking to any developer. The skill is knowing what to build, not knowing how to code.2. Four agent components - Every agent that works has intelligence (model), tools (actions), memory (session context), and knowledge (your company data). Every agent that disappoints is missing at least one.3. n8n for foundations - n8n is the best learning tool because you visually see every component of the agent architecture as separate nodes. Build your first multi-agent system and evaluation pipeline here.4. Claude Code ate three company types - Context companies, action companies, and evaluation companies all got replaced by one agentic loop inside Claude Code. The three pieces collapsed into one tool.5. Computer control is the real unlock - File system access plus bash commands equals full laptop capability. This is why Claude Code went from coding tool to work operating system.6. Long-horizon jobs changed the game - AI agents went from 3-minute tasks to 3-6 hour sustained jobs in six months. This turns Claude Code from assistant to autonomous worker.7. Continuous learning loops - Build a second agent that watches your corrections to the first agent's work. After five repeated patterns, it proposes a skill update. Your tools get better every day.8. OpenClaw pattern - Delegation through existing channels, full machine sandboxing, model-agnostic. Not a product but a pattern that Google and AWS will copy inside their ecosystems.9. AI PM interviews changed - At L5 and L6, product sense questions are being replaced with live building exercises and system design for AI architectures. Pull out Claude Code during the interview or you are already out.10. Compensation trajectory - From $120K at Microsoft to $1.3M at Google over 13 years, doubling every 18 months through AI-focused switches. Left because big companies kill innovation with six-week approval cycles.----Where to find Mahesh Yadav* LinkedIn* Maven CourseRelated contentPodcasts:* Claude Code Team OS with Carl Vellotti* OpenClaw + Claude Code with Naman Pandey* Claude Code OS with Dave KilleenNewsletters:* The complete context engineering guide* How to use Claude Code like a pro* Practical AI agents 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 episodeThe design process you learned is already dead.Most teams still follow the same linear pipeline. Low fidelity to high fidelity to handoff. Sketch it. Spec it. Ship it over the wall. That pipeline was built around a constraint that no longer exists. High fidelity used to be expensive. It is not anymore.I brought in two people who represent both sides of the new design infrastructure.Ed Bayes is a member of the design staff at OpenAI. He leads design on Codex, which just crossed 2 million weekly users with usage surging 3X since the start of the year. He spends 70-80% of his time coding. He still calls himself a designer.Gui Seiz is the Director of Product Design for AI at Figma. He leads design on all their AI features, including the Figma MCP server and Figma Make. His designers are now shipping PRs to production.----Check out the conversation on Apple, Spotify, and YouTube.Brought to you by:* Bolt: Ship AI-powered products 10x faster* Amplitude: The market-leader in product analytics* Pendo: The #1 software experience management platform* NayaOne: Airgapped cloud-agnostic sandbox* Product Faculty: Get $550 off their #1 AI PM Certification with my link----If you are trying to understand the new design workflow, this is the one episode to watch.If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.I’m putting on a free webinar on Behavioral and AI PM interviews. Join me.----Key Takeaways:1. Code vs canvas is a false dichotomy - The best designers use both fluidly. Canvas for exploration, collaboration, and pixel-perfect craftsmanship. Code for interactions, responsive testing, and the last mile of polish. The question is what you are trying to learn, not which tool to commit to.2. High fidelity is no longer expensive - The entire linear design process existed because building something interactive required engineering resources. That constraint is gone. A functional wireframe takes the same time as a paper sketch.3. The Codex-Figma MCP makes handoff lossless - Import screens from a running React app into Figma with exact pixel values. Border radius, padding, shadows, all one to one. It is not a screenshot. It is a responsive, editable design artifact.4. The reverse direction works seamlessly - Make changes in Figma, paste a component link into Codex, and it updates your code automatically. No redline spec, no handoff document.5. Ed spends 70-80% of his time coding and still calls himself a designer - The medium changed but the mandate did not. Designers are still the voice of the user, still upholding craft. The tools expanded, the role stayed.6. Figma designers are shipping PRs to production - Teams that six months ago were AI curious are now banging down the door. Monetization designers who never wrote code are building technically complex prototypes.7. "Prototypes, not PRDs" is the emerging norm - PMs at OpenAI bring working prototypes to design reviews. They ship PRs to stress-test ideas before handing off to engineering.8. You do not need permission to start - Someone from OpenAI's GTM team built an iOS app with zero experience. Download Codex and build something for yourself tonight.9. Curiosity is the defining skill for this era - Not code proficiency, not design talent. The AI is an infinitely patient tutor. Ask questions. Build understanding alongside output.10. Total football is the mental model - Every player can play every position. Roles still have natural spikes. But the tool constraints that enforced rigid boundaries are dissolving.----Where to find Ed Bayes* LinkedIn* OpenAI* XWhere to find Gui Seiz* LinkedIn* Figma* XRelated contentPodcasts:* Xinran Ma - Design with AI* Carl Vellotti - Claude Code PM OS* Codex PM Guide with Carl VellottiNewsletters:* AI prototyping for PMs* The PM guide to Bolt* Codex PM guide----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 way PM teams are trending, one PM is going to support 20 people.Not just engineers. Designers. Analysts. Strategy partners. GTM. Sales. Support.You cannot answer everyone’s questions about everything. You cannot be in every Slack thread. You cannot be the bottleneck for context that already exists somewhere in a Google Doc no one can find.But you can give them a high-context, well-organized repo.Hannah Stulberg is a PM at DoorDash and a former Google PM. She has spent over 1,500 hours in Claude Code.She wrote the viral Claude Code for Everything series. Her setup is not a personal productivity system. She has structured her entire team’s context into a shared repo that everyone queries.Her strategy partner - completely non-technical - puts up pull requests every day. Her engineers query metric definitions without asking the analyst. Her designers pull product context without waiting on a PM.If you are building a team that runs on AI, this is the episode to watch.----Check out the conversation on Apple, Spotify, and YouTube.Brought to you by:* Bolt: Ship AI-powered products 10x faster* Jira Product Discovery: Plan with purpose, ship with confidence* Kameleoon: Leading AI experimentation platform* Amplitude: The market-leader in product analytics* Product Faculty: Get $550 off their #1 AI PM Certification with my link----If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.I’m putting on a free webinar on Behavioral and AI PM interviews. Join me.----1. Build a Team OS, not a personal OS - A shared repo where every function checks in work. Engineers, designers, and analysts self-serve without asking the PM.2. Root CLAUDE.md is everything - Doc index, team roster with Slack IDs, channel map. Keep under one page or you burn context every session.3. Nested indexes save 97% of context - Every folder gets a navigation CLAUDE.md. A customer query used only 3% of the context window.4. Three token tiers - Always-loaded root (~500 tokens), folder indexes on navigation (200-500), content files on demand (1,000-10,000+).5. Split analytics by product area - Metrics, queries, schemas separated. Progressive loading prevents waste.6. Gate launches on repo updates - Feature not shipped until metrics, queries, schemas, and playbooks are checked in.7. Verified playbooks kill hallucinations - Analyst-audited methodology. Claude follows verified steps instead of inventing its own.8. Plan mode makes 10x docs - Shift+Tab twice. Five phases: load context, ask questions, build plan, push thinking, review agents.9. Split long docs across parallel agents - Each writes to a temp file. Orchestrating agent compiles. Prevents context overflow.10. The flywheel compounds daily - Automate one task, free time, improve the repo. After 1,500 hours still iterating every day.----Where to find Hannah Stulberg* LinkedIn* In the Weeds SubstackRelated contentPodcasts:* My Claude Code PM OS with Dave Killeen* Claude Code + Analytics with Frank Lee* Claude Code as PM OS with Carl VellottiNewsletters:* The ultimate guide to context engineering* Build your PM operating system* How to use Claude Code like a pro----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 episodeClaude Code just hit $2.5 billion in annualized revenue in 9 months.It is the fastest B2B software product ramp in history.So why are most people still using it like a chatbot?This is how most people use Claude Code. Type a prompt and get output. The context fills up. It compacts. You lose everything. You start over.The top users flipped it. They built skills that interview through a framework before building anything. They use sub-agents that preserve context. They have operating systems where every file, every person, every project has a home.That shift is what today’s episode is about.I sat down with Carl Vellotti for the third time. His first episode was the beginner course. His second episode was the advanced masterclass. Together they crossed over a million views across platforms.Today is the operating system layer. If you are already an 80 out of 100 on Claude Code, this episode will bring you to a 95 out of 100.This episode covers context management, creating sub-agents to manage your context for you, auto-triggering skills with hooks, trustworthy data analysis with Jupyter notebooks, and building an operating system around it all.If you are living in Claude Code 8 to 10 hours a day and want to stop fighting the tool, this is the one episode to watch.----Check out the conversation on Apple, Spotify, and YouTube.Brought to you by:* Bolt: Ship AI-powered products 10x faster* Amplitude: The market-leader in product analytics* Pendo: The #1 software experience management platform* NayaOne: Airgapped cloud-agnostic sandbox* Product Faculty: Get $550 off their #1 AI PM Certification with my link----If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.I’m putting on a free webinar on Behavioral and AI PM interviews. Join me.----Key Takeaways:1. Context management is the real skill - A single web search eats 10% of your context. Run /context to see what is consuming it. System prompt and MCPs take 10-16% before you type one message.2. Sub-agents save 20x context - Delegate research to a sub-agent. Same task costs 0.5% instead of 10%. Your main session only gets the summary.3. Replace MCPs with CLIs - MCPs eat context by existing. CLIs have zero overhead. GitHub CLI, Vercel CLI, Google Workspace CLI are all dramatically more efficient.4. Powerful skills need zero code - Anthropic's front-end design plugin is just a good prompt. No APIs or tooling. Just rules that tell Claude "do not look like AI."5. Give Claude self-checking tools - The make slides skill uses Puppeteer to screenshot output, measure overflow, and fix issues before you see them.6. Repeat prompts for better quality - A Google paper showed pasting a prompt twice helps. Tell Claude to double-check against skill instructions after the first pass.7. Use hooks to auto-invoke skills - A user_prompt_submit hook matches your words against skill keywords instantly. Zero context cost.8. Jupyter notebooks solve data trust - Every analysis shows exact code, inputs, and outputs. Traceable and reproducible.9. Build an operating system - Knowledge folder for people context. Projects folder for task isolation. Tools folder for scripts. CLAUDE.md for identity.10. The people folder compounds - Connect meeting transcription. After every meeting, update each person's dossier. Every prompt gets more specific over time.----Related contentPodcasts:* Claude Code Masterclass with Carl Vellotti (Ep 2)* Claude Code PM OS with Dave Killeen* OpenClaw Setup Guide with Naman PandeyNewsletters:* The ultimate guide to context engineering* How to use Claude Code like a pro* Claude Cowork and Code setup guidePS. Please subscribe on YouTube and follow on Apple & Spotify. It helps! 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 episodeStop applying to AI PM jobs until you understand the fundamentals.That is not gatekeeping. That is the MIT finding. 19 out of 20 AI pilots fail. The #1 reason? Picking the wrong problem to apply AI to.Not the wrong model. Not the wrong data. The wrong problem.Jyothi Nookula has spent 13.5 years in AI. 12 patents. AIPM at Amazon (SageMaker), Meta (PyTorch), Netflix (Developer Platform), and Etsy.She has hired AIPMs at three of those companies. Trained 1,500+ PMs to transition into AI roles.If you are trying to break into AI PM, this is the one episode to watch.----Brought to you by* Product Faculty: Get $550 off their #1 AI PM Certification with my link* Amplitude: The market-leader in product analytics* Pendo: The #1 software experience management platform* NayaOne: Airgapped cloud-agnostic sandbox for AI validation* Kameleoon: Prompt-based experimentation for product teams----If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.If you want my PM Operating System in Claude Code, click here.----Key Takeaways:1. Two types of AIPM roles exist - 80% are traditional PM roles with AI features added on, where the core product existed before AI. 20% are AI native roles where the product IS AI and the value proposition is impossible without it. Know which type before you apply.2. The AI PM stack has three layers - Application PMs own user experience (60% of roles, easiest entry point). Platform PMs build tools for other builders (30%). Infra PMs build foundational systems like vector databases and GPU orchestration (10%).3. 19 out of 20 AI pilots fail from wrong problem selection - AI makes sense for complex pattern recognition, prediction from historical data, and personalization at scale. If explainability is non-negotiable, rules exist, data is limited, or speed is critical, start with heuristics.4. Most teams overcomplicate their AI technique choice - If you can put the problem in a spreadsheet with inputs and an output to predict, traditional ML is the answer. Perception problems need deep learning. Natural language reasoning needs Gen AI. These are not competitors, they are tools in your toolkit.5. AI products are fundamentally probabilistic - The same input can produce different outputs. AIPMs must think in quality distributions and acceptable error rates, not binary success vs failure. Data is a first-class citizen, not a nice-to-have.6. Agents decide, workflows follow steps - Workflows have predetermined sequences with deterministic outcomes. Agents receive goals and independently decide which tools to use. The live N8N demo showed identical tools producing completely different execution patterns.7. Context engineering is the real production skill - Claude Sonnet has a 200K token context window but that fills fast with knowledge bases, conversation history, and real-time data. Every token costs money. Managing what to load and when directly impacts both quality and cost.8. Follow the hierarchy before fine tuning - Prompt optimisation first, then context engineering, then RAG. 80% of use cases get solved with RAG. Fine tuning should only be considered after exhausting all three.9. Build products not projects - Launch your AI work, get real users, encounter real breakage. That gives you richer interview material than any course certificate. Build an agent, build a RAG system, and build an app that solves a real problem.10. PM culture at big tech shapes who you become - Amazon PMs spend 40-50% of time writing PRFAQs and six-pagers. Meta PMs live in experimentation and statistical significance. Netflix PMs operate with full autonomy through context over control. Each teaches something different.----Where to find Jyothi Nookula* LinkedIn* NextGen Product ManagerRelated contentPodcasts:* Naman Pandey on OpenClaw* Lisa Huang on Gemini Gems* Frank Lee on Amplitude and MCPNewsletters:* The ultimate guide to context engineering* RAG vs fine tuning vs prompt engineering* AI foundations for PMsPS. Please subscribe on YouTube and follow on Apple & Spotify. It helps! 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 episodeMost PMs treat evals like a quality gate. Something you run right before shipping, just to check the box.That is backwards.The best AI product teams treat evals as the starting point. They write the eval before the prompt. They iterate on the scoring function before the model. They use failing evals as a roadmap.That shift is what today’s episode is about.I sat down with Ankur Goyal, Founder and CEO of Braintrust. It is the eval platform used by Replit, Vercel, Airtable, Ramp, Zapier, and Notion. Braintrust just announced its Series B at an $800 million valuation.Users are running 10x more evals than this time last year. People log more data per day now than they did in the entire first year the product existed.In this episode, we build an eval entirely from scratch. Live. No pre-written prompts, no pre-written data. We connect to Linear’s MCP server, generate test data, write a scoring function, and iterate until the score goes from 0 to 0.75.Plus, we cover the complete eval playbook for PMs:If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.If you want my PM Operating System in Claude Code, click here.----Check out the conversation on Apple, Spotify, and YouTube.Brought to you by:* Kameleoon: Leading AI experimentation platform* Testkube: Leading test orchestration platform* Pendo: The #1 software experience management platform* Bolt: Ship AI-powered products 10x faster* Product Faculty: Get $550 off their #1 AI PM Certification with my link----Key Takeaways:1. Vibe checks are evals - When you look at an AI output and intuit whether it is good or bad, you are using your brain as a scoring function. It is evaluation. It just does not scale past one person and a handful of examples.2. Every eval has three parts - Data (a set of inputs), Task (generates an output), and Scores (rates the output between 0 and 1). That normalization forces comparability across time.3. Evals are the new PRD - In 2015, a PRD was an unstructured document nobody followed. In 2026, the modern PRD is an eval the whole team can run to quantify product quality.4. Start with imperfect data - Auto-generate test questions with a model. Do not spend a month building a golden data set. Jump in and iterate from your first experiment.5. The distance principle - The farther you are from the end user, the more critical evals become. Anthropic can vibe check Claude Code because engineers are the users. Healthcare AI teams cannot.6. Use categorical scoring, not freeform numbers - Give the scorer three clear options (full answer, partial, no answer) instead of asking an LLM to produce an arbitrary number.7. Evals compound, prompts do not - Models and frameworks change every few months. If you encode what your users need as evals, that investment survives every model swap.8. Have evals that fail - If everything passes, you have blind spots. Keep failing evals as a roadmap and rerun them every time a new model drops.9. Build the offline-to-online flywheel - Offline evals test your hypothesis. Online evals run the same scorers on production logs. The gap between them is your improvement roadmap.10. The best teams review production logs every morning - They find novel patterns, add them to the data set, and iterate all day. That morning ritual is what separates teams that ship blind from teams that ship with confidence.----Where to find Ankur Goyal* LinkedIn* BraintrustRelated contentNewsletters:* AI evals explained simply* AI observability for PMs* How to build AI productsPodcasts:* AI evals with Hamel Husain and Shreya Shankar* AI evals part 2 with Hamel and Shreya* Aman Khan on AI product quality----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

This is a free preview of a paid episode. To hear more, visit www.news.aakashg.comToday’s episodeEvery PM I talk to is using AI the same way. Open Claude. Type a question. Get an answer. Close the tab.The AI does nothing while you sleep. It forgets everything the next morning. It cannot touch your Slack, your email, your file system.OpenClaw changes that.245,000 GitHub stars. 2 million weekly visitors. Peter Steinberger built it, Sam Altman bought it for over a billion dollars. I covered what OpenClaw is and why it matters when it first went viral. Today’s episode goes deeper. A complete, step-by-step installation and five PM automations you can copy.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by* Jira Product Discovery: Plan with purpose, ship with confidence* Vanta: Automate compliance, manage risk, and prove trust* Mobbin: Discover real-world design inspiration* Maven:* Product Faculty: Get $550 off their #1 AI PM Certification with my link----If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.If you want my PM Operating System in Claude Code, click here.----Key Takeaways:1. OpenClaw is a proactive AI agent, not a reactive chatbot - Unlike ChatGPT or Claude, OpenClaw runs as a continuous daemon on your machine. It executes tasks at 3 a.m. while you sleep, maintains persistent memory across sessions, and acts autonomously based on scheduled cron jobs.2. Installation takes three terminal commands - NPM install, openclaw onboard, and hatch the bot. If you do not see red text in the terminal, the installation worked. Yellow warnings are normal and safe to ignore.3. The Slack integration has one critical step everyone misses - Every time you change bot permissions in the Slack API console, you must click Reinstall to Workspace. Without this step, no permission changes persist and the bot appears broken.4. The workspace docs folder is your team's knowledge base - Drop PRDs, FAQs, and product docs into the local .openclaw/workspace/docs folder. Any team member can query the entire repository by mentioning the bot in any Slack channel, and the bot can write back to the docs.5. Cron jobs replace manual PM rituals - Set up a morning stand-up summary that scans Slack channels overnight and posts a brief at 9 a.m. with what shipped, active blockers, and customer complaints. You describe it in English and OpenClaw writes the code.6. Competitive intelligence runs on autopilot - OpenClaw can monitor competitor websites, reviews, and mentions every 30 minutes and post SWOT analyses to a private Slack channel. It tracks changes over time for trend analysis months later.7. Voice of customer reports aggregate every feedback source - Connect Slack support channels, email, Google reviews, Reddit, and more. OpenClaw scans every 30 minutes and synthesizes a weekly report automatically.8. Smart bug routing checks customer tier automatically - OpenClaw reads bug reports, looks up the reporter in a customer CSV, escalates enterprise bugs to engineering immediately, and routes free-tier bugs to design as low priority.9. Security audit is non-negotiable before going live - Tell OpenClaw to analyze its own security vulnerabilities. It will flag unrestricted file access, disabled firewalls, and missing approval gates. Set up a weekly cron job to run the audit automatically.10. Local deployment is safest for most PMs - A VPS gives 24/7 uptime but removes your physical kill switch. A dedicated Mac Mini is the most recommended option. Local deployment on your laptop is the safest because the bot sleeps when you close your laptop.----Related contentNewsletters:* OpenClaw complete guide* My PM Operating System* The AI PM Tool StackPodcasts:* Claude Code PM OS with Dave Killeen* Claude Code + Analytics with Frank Lee* Gemini Gems Masterclass with Lisa Huang----PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!If you want to advertise, email productgrowthppp at gmail.

Today’s episodeMost PMs start every day like this. Open the calendar. Open the CRM. Open Slack. Open the meeting notes. Open LinkedIn. Piece together what matters. Lose 30 minutes before real work even starts.That is not how the best PMs are working anymore. The best PMs are running one command in the morning and getting everything they need in five minutes. Their calendar, their deals, their market intel, their career gaps, all pulled together automatically.That shift is what today’s episode is about.I sat down with Dave Killeen, Field CPO at Pendo.io. He has worked at BBC, Mail Online, and now runs the field product function at one of the largest product management platforms in the world. He has 25 years in product. Over the last few months, he built a full personal operating system called DEX in Claude Code, open sourced it on GitHub, and it is getting serious traction.In this conversation, Dave walks through his entire system live on screen. You will see how he runs a daily plan, creates PRDs from a backlog, manages parallel workstreams on a Kanban board, and tracks his career goals, all from one terminal window. And you will learn the three building blocks that make it all work.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by* Pendo: The #1 software experience management platform* Jira Product Discovery: Plan with purpose, ship with confidence* Amplitude: The market-leader in product analytics* NayaOne: Airgapped cloud-agnostic sandbox* Product Faculty: Get $550 off the AI PM Certification with code AAKASH550C7----If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.----Key Takeaways:1. One command replaces your morning routine - Dave's daily plan slash command pulls from calendar, CRM, Granola, LinkedIn, YouTube, and 120 newsletters in five minutes. No tab switching. No manual assembly.2. MCP servers are the key to connecting everything - Point Claude at any API documentation with your API key and it builds an MCP server for you. MCP provides structured guardrails that make the AI's behavior consistent and deterministic.3. Skills, MCP, and hooks are three different things - Skills are plain English job descriptions for what the AI should do. MCP servers are structured integrations for connecting external services. Hooks are triggers that fire at specific conversation moments.4. Session start hooks make the system compound - Every new Claude Code chat gets injected with weekly priorities, quarterly goals, working preferences, and past mistakes. The AI never starts from scratch.5. Living markdown files are the compounding mechanism - Every project, person, and company gets a markdown file that accumulates context from meetings, messages, and intel over time. The more you use the system, the smarter every file becomes.6. You can build a mobile app in 37 minutes - Dave built the full app with Claude and spent more time in Xcode publishing it. The constraint is taste, not building speed.7. The AI should hold you accountable - Dave's Claude MD file includes "harsh truths for Dave" that the AI wrote after auditing his system. This gets injected into every session to prevent the same mistakes.8. Career planning should compound like product data - A career MCP server collects evidence, runs gap analysis, and calculates promotion readiness. When review time comes, the evidence is already assembled.9. Be precise about your goal, not the path - The kindest thing you can do for the AI is give it a very clear destination. Do not tell it how to get there. Let it figure out the most elegant approach itself.10. Voice-first changes everything - Using Whisperflow or Super Whisper instead of typing fundamentally changes how you interact with Claude. You think out loud. The conversation flows. You build faster.----Where to find Dave Killeen* LinkedIn* Pendo----Related contentNewsletters* The PM operating system guide* How to use Claude Code like a pro* Master AI agent distribution* Claude Cowork and Code setup guide* The AI PM tool stackPodcasts* Frank Lee on Claude Code and MCP workflows* Carl Vellotti on Claude Code operating systems* Rachel Wolan on AI PM workflows* Caitlin Sullivan on building with Claude----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 episodeMost PMs are using AI the same way they used Google in 2005.Type something in. Get something out. Move on.That is not how the best PMs are using it. The best PMs have stopped treating AI as a search engine and started treating it as a team member. One that already knows their product, their writing style, their strategy. One that does not need to be briefed from scratch every single time.That shift is what today’s episode is about.I sat down with Lisa Huang, SVP of Product at Xero, an $18 billion finance platform. She built the AI assistant for the first generation Meta RayBan smart glasses. She created Gemini Gems at Google. She has been an AI PM at Apple, Meta, and Google - three of the most demanding AI product environments in the world.She gave us a masterclass across Gemini Gems, building AI into hardware, running AI agents at scale inside a financial product, and what the AI PM career actually looks like from here.In today’s episode, we discuss across three topics.* How to build Gemini Gems and AI projects that actually work.* What she learned building AI into a wearable device.* What the future of the AI PM career actually looks like.----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----If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.----Key takeaways:1. Stop briefing your LLM from scratch every time - Gemini Gems hold your context permanently. Your role, your company strategy, your writing style. Build it once and it already knows everything the next time you open it.2. Every PM needs 3 Gems - A writing clone trained on your PRDs and emails. A product strategy advisor loaded with your company docs and competitor analysis. A user research synthesizer that ingests raw transcripts and surfaces key themes.3. Vague instructions are the number one mistake - "Help me write better" gets you nothing. Write a full page of context. Your role, your audience, your format preferences. The more specific, the more personalized the output.4. Gemini Gems vs ChatGPT custom GPTs - OpenAI framed GPTs as an app store ecosystem. Google focused on personal productivity instead. First principles beat copying a competitor's framing, and the GPT store never took off.5. On-device AI is the future for wearables - Cloud is the default today but once a device is on your face all day, people want their data staying local. Privacy beats performance when the device is that personal.6. Accuracy is the product in high-stakes AI - LLMs out of the box are not great at math, accounting, or tax. Winning agents combine deep domain knowledge with proprietary data that no general-purpose model can access.7. Measure agents in three layers - Quality first (evals, human annotators, LLM judges). Product metrics second (adoption, retention, CSAT). Business impact third (revenue attribution, ARR). Skip to layer three without the foundation and you are measuring on sand.8. AI will not replace PMs - it will replace the execution work. Writing PRDs, creating mocks, managing roadmaps. What stays is product judgment. The ability to look at ambiguous signals and say this is the right bet and here is why.9. The PM role is becoming a hybrid - PM to engineer ratios will compress. The expectation is that PMs also build. Not just spec and hand off, but prototype, design, and code enough to show what they mean. The tools to do this exist right now.10. Your company's permission is not required - Most companies are not fine-tuning models. They are using the same consumer tools you already have. Build Gems. Build projects. Build small AI products with your personal data. There is nothing stopping you.----Where to find Lisa Huang* LinkedIn* WebsiteRelated contentNewsletters* How to become an AI PM* Practical AI agents for PMs* AI evals explained simply* AI product strategy* The AI PM learning roadmapPodcasts* Claude Code + Analytics - Vibe PMing with Frank Lee* AI evals explained simply with Ankit Shukla* How to become an AI PM with Marily Nika* AI prototyping mastery with Sachin Rekhi----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 episodeAI prototyping tools are redefining what it means to be a PM.Bolt went from 0 to $40M ARR in 4.5 months. Lovable hit $17M ARR in 3 months. Every forward-thinking product team is starting to prototype earlier, faster, and at higher fidelity than ever before.But most PMs are using these tools wrong.They open Bolt or Lovable, type a vague prompt, get something that looks decent, show it around, and move on. No problem space work. No divergent solutions. No user testing. The prototype dies in a Slack thread and nothing changes.In this episode, we built a LinkedIn sentiment analysis feature from scratch - live - to walk you through the complete workflow. From blank page to multi-page, clickable, high-fidelity prototype. We covered when to prototype, how to prompt, when to go high fidelity, and how to hand off to engineers with zero open questions.If you watch, you’ll also learn why your PRD and prototype need to live together - and why that combination is the new standard for forward-thinking PMs.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:* Pendo: The #1 software experience management platform* Testkube: Leading test orchestration platform* Gamma: Turn customer feedback into product decisions with AI* Product Faculty: Get $550 off the AI PM Certification with code AAKASH550C7* Mobbin: Discover real-world design inspiration----If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.----Key Takeaways:1. AI prototyping doesn't replace problem space work - it accelerates solution space work. Before opening any prototyping tool, lock down the problem, the user story, and the rough shape of the solution. If you can't write all three in one paragraph, you're not ready.2. Always start from your design system, not a blank page - Drop a screenshot of your existing product and ask the tool to recreate it. Save that as a team template. Every prototype you build from that point looks like it belongs in the product.3. Build 3 to 4 divergent solutions before choosing one - The entire point of AI prototyping is that building a second and third version costs almost nothing now. We built two versions of the sentiment analysis feature live. Neither was perfect. Both were useful. That comparison is the point.4. Use visual editing for fine-tuning, not prompting - Once you've picked the strongest direction, switch to direct visual editing. Move elements, match colours with the eyedropper, adjust spacing. It's faster because the result is immediate.5. Single-page prototypes miss too much - Build the full end-to-end flow. The moment you start connecting pages, edge cases surface automatically. We found two edge cases in minutes that would have cost engineering time in sprint.6. Prompt clarity beats prompt engineering - Any ambiguity in your prompt will get exploited statistically. Before running a complex prompt, paste it into a separate chat and ask it to find the contradictions. Fix those first.7. Use discuss mode before building anything major - Don't ask the AI if it can do something. That always gets a yes. Ask what it thinks the right approach is. The answer is far more honest and useful.8. High fidelity is for selling and usability testing - Low fidelity is for team exploration. Any prototype going in front of users needs to feel real, otherwise you get feedback about the roughness, not the experience.9. The PRD and prototype should live together - The PRD covers edge cases, empty states, error conditions. The prototype covers the 90% flows. Together they leave zero open questions for engineers. If someone reads both and still has a question, something is missing.10. The prototype is already standard code - A functional prototype built in Dazzle is a full server-side and client-side application. Download the project folder, drop it next to the production codebase, and tell Cursor to copy the interaction. Most of the implementation gets handled automatically.----Related contentNewsletters* Product Requirements Documents (PRDs): a modern guide* Ultimate guide to AI prototyping tools (Lovable, Bolt, Replit, v0)* Your guide to AI product strategy* AI PRDs: everything you need to know* AI agents: the ultimate guide for PMsPodcasts* The most powerful AI workflow for PMs with Frank Lee* How to engineer delight into AI products with Nazarin Shenel* AI prototyping tools with Eric Simons, CEO of Bolt----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 is a term Andrej Karpathy coined last year: vibe coding.We have the same for product management: Vibe PMing.You describe the problem. The agent pulls the data. Analyzes the chart. Synthesizes the feedback. Drafts the spec. Files the ticket.That is not theory. That is what I walked through in today’s episode with a principal PM at Amplitude who builds MCP and agent products for a living. He showed it live, on screen, in real time.If you tune in, you’ll learn the full end-to-end workflow:----If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:* Amplitude: The market-leader in product analytics* Pendo: The #1 software experience management platform* Testkube: Leading test orchestration platform* Product Faculty: Get $550 off the AI PM Certification with code AAKASH550C7* Bolt: Ship AI-powered products 10x faster----Key Takeaways:1. Claude Code + MCP is the most powerful AIPM workflow today - Connect your analytics tool via MCP, load your product context into a repo, and let the agent do analysis that used to take hours in minutes.2. Deep chart analysis now takes 90 seconds instead of 3 hours - Drop a chart URL into Claude Code, trigger the analyse chart skill, and the agent navigates your data taxonomy, finds anomalies, and hypothesises why metrics changed.3. Automate your entire weekly business review - Point Claude Code at your dashboards Monday morning. Get 3-5 top insights and the one urgent issue to tackle — no manual dashboard scanning ever again.4. Customer feedback synthesis across all channels in one pass - Zendesk, Gong, Salesforce, Slack, app stores all unified. Claude Code navigates the MCP, clusters themes, and surfaces what customers love and hate that week.5. PRDs write themselves from insights - Take the analysis output, point it at your PRD template in Cursor or Claude Code, and get a first draft spec in under 2 minutes. Iterate with command L or command K.6. Skills are the most important Claude Code feature - A skill is just a named prompt with heuristics and tool instructions. It loads only when relevant, preventing context bloat and giving the agent a repeatable workflow.7. The biggest MCP mistake is connecting too many servers - Every tool description burns context. Load only what's relevant to the workflow. Remove or hide tools that aren't being used for a given task.8. MCP is not for complex orchestration — it's for data access - Set the right expectation. MCP connects AI to external systems easily. It's the first step, not the whole pipeline.9. Granola has no MCP, so build a script instead - Frank used Claude Code to write a local script that dumps Granola meeting notes into his product repo. Now he can pull all meeting context with a single at-command.10. The future of PMing is vibe PMing - Chart analysis, dashboard reporting, feedback synthesis, spec writing, and prototyping — all agent-driven. PMs who adopt this workflow now will have a massive advantage in 2-3 years.----Related contentNewsletters:* How to use Claude Code like a pro* Steal 6 of my Claude skills* Context engineering* The AI stack for PMs* Practical AI agents for PMsPodcasts:* How to build an AI-native PM operating system with Mike Bal* AI evals explained simply with Ankit Shukla* Advanced guide to AI prototyping with Sachin RekhiPS. Please subscribe on YouTube and follow on Apple & Spotify. It helps! 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 EpisodeDesigning with AI isn’t about prompting.Most PMs think they understand AI design because they can write a good prompt. They’re wrong.Real AI design is about understanding the entire workflow, the system, the constraints, and the behaviors.Xinran Ma runs Design with AI, one of the top newsletters on AI design. He’s been studying AI design tools for three years. And he hasn’t shared most of this information publicly before.In today’s episode, we’re going live. We’re building real prototypes. We’re showing you the exact workflows that top 1% designers use.By the end of this episode, you’ll know the entire workflow from PRD to prototype to product.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:* NayaOne: Airgapped cloud-agnostic sandbox* Pendo: The #1 software experience management platform* Maven: The cohort-based course platform powering the future of learning* Bolt: Ship AI-powered products 10x faster* Gamma: Turn customer feedback into product decisions with AI----If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.----Key takeaways:Key Takeaways:1. AI design covers five areas not just prompts - Prompting, ideation, design/prototyping, workflows, and staying conscious. Most people think better prompts equal better design. That's just 20% of the skill.2. Use Google AI Studio for quick design variations - Upload 2-3 visual references. Describe what you want. Generate three different design directions in 5 minutes. What used to take 3-4 hours now takes 15 minutes.3. Lovable builds functional prototypes in seconds - Describe the experience you want to build. Lovable generates a working prototype in 60 seconds. Not mockups—actual clickable experiences you can test with users.4. Match tools to specific use cases - Custom GPT for effective prompts. Lovable for high-quality prototypes. Magic Patterns for design variations. Google AI Studio for free exploration. Cursor for full-stack experiences. Claude Code as all-purpose best.5. Good design passes four layers not just visual - Visual representation, problem-solving, design principles, and implementation feasibility. Most people stop at layer one. Great design works at all four layers.6. Context matters more than prompt length - Don't say "design a button." Say "design a primary CTA button for B2B SaaS onboarding where users connect calendar. Professional brand." Specificity drives quality.7. Visual references anchor AI output - Upload 2-4 screenshots showing the aesthetic you want. These show AI what "modern and minimal" means to you. The quality difference is massive versus text-only prompts.8. Iteration speed determines final quality - The magic isn't in the first output. It's in the 10th iteration after you've refined and tweaked. Review, identify issues, tell AI how to fix, repeat.9. Always validate with real users - AI tools make generating designs easy. Only users tell you if those designs actually help. Show prototypes to 3-5 users. Watch them try to use it.10. Workflows changed from linear to parallel - Before AI: sequential steps taking weeks. After AI: describe, generate, iterate freely. This is how top 1% designers work now.----Where to Find Xinran* LinkedIn* Newsletter* Maven courseRelated ContentNewsletters:* AI Prototyping Tutorial* AI Prototype to Production* How to Build AI Products* Prompt Engineering* Product Requirements DocumentsPodcasts:* Advanced Guide to AI Prototyping with Sachin Rekhi* AI Prototyping for PMs* How to Become an AI PM* Everything You Need to Know About AI----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

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 BUILDToday’s EpisodeAnkit Shukla is BACK after his gangbusters episode, that is my #2 most popular of all time. This time he's diving deep on one of the most important new AI skills for PMs: Evals.Whether you're working on AI features now or not, this is a skill you want to have an intuitive understanding of. So, I'm building on my library of eval episodes with today's drop.I've never heard someone explain evals from first principles as intuitively as Ankit has with this one. Hope you enjoy as much as I did!If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.Where to find Ankit Shukla* HelloPM* Twitter (X)* LinkedIn* YouTubeRelated ContentNewsletters:* AI Evals* AI Testing* LLM JudgesPodcasts:* How to Do AI Evals Step-by-Step with Real Production Data* The PM’s role in AI Evals* AI Evals LivePS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!email productgrowthppp at gmail dot com for sponsorships. 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 EpisodeDiscovery might be the most important core PM skill for building great products.But most PMs are unprepared to do discovery in AI. PMs run surveys incorrectly, conduct interviews poorly, and end up with poor insights.Today will give you the roadmap to avoid all those mistakes.Caitlin Sullivan is a user research expert who runs courses teaching PMs how to do AI-powered discovery. And in today’s episode, she shows you exactly how she does it.We’re talking live demos. Step-by-step workflows. Real survey data. Real interview transcripts.This is a masterclass in discovery. The kind that moves the needle.----Brought to you by:Maven: Get 15% off Caitlin’s courses with code AAKASHxMAVENPendo: The #1 software experience management platformJira Product Discovery: Plan with purpose, ship with confidenceKameleoon: AI experimentation platformAmplitude: The market-leader in product analytics----Key Takeaways:1. Replicate the human process - Good AI analysis mirrors how experienced researchers work: comb through data first, then synthesize. Never jump straight to "give me themes."2. Use multi-step prompting - Load context in one prompt, run per-participant analysis in the next, then verify. Cramming everything into one prompt degrades quality.3. Code before you count - For surveys, apply inductive coding labels to every response before asking for patterns. Skipping this step leads to miscategorized, unreliable results.4. Always audit AI's work - Force the model to re-check its own analysis. It catches contradictions, overexaggerated intensity ratings, and miscoded responses regularly.5. Claude wins on nuance, Gemini wins on frequency - Claude gives more thorough, complete analysis by default. Gemini surfaces top-frequency themes faster but misses smaller patterns.6. Define everything explicitly - Quotes, ratings, emotional intensity levels, contradiction types. If you assume the model shares your definitions, you'll get inconsistent results.7. Markdown files beat raw transcripts - Converting transcripts to structured markdown improves accuracy and helps you work around token limits on non-Max plans.8. Parallelize with Claude Code agents - Set up agent markdown files for interview and survey analysis, then run both simultaneously. Cuts total analysis time in half again.----Related ContentNewsletters:How to Do Product Discovery RightAdvanced Techniques: Continuous DiscoveryCustomer Interviews: Advanced TechniquesPodcasts:Teresa Torres’ Guide to AI DiscoveryComplete Course: AI Product DiscoveryUltimate Guide to Knowing Your Users as a PM----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 EpisodeMost PMs are drowning in tools.You log into JIRA. Then Figma. Then Confluence. Then Notion. Then Google Analytics. Then Slack.Twenty different tabs. Twenty different logins. Zero flow state.Mike Bal runs product at David’s Bridal, a company undergoing massive digital transformation.And he operates from a single interface.Cursor and Claude Desktop sit at the center. Everything else connects through MCP and custom integrations.Research? Manus feeds into Claude. Analytics? Clarity exports into Cursor. Design? Figma pulls directly into his projects.This isn’t a tool stack. It’s an operating system.Today, Mike shows you exactly how to build it.----If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.Are you searching for a PM job? Join me + 29 others for an intensive 12-week experience to master getting a PM job. Only 9 seats left.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by Linear: Plan and build products like the best.----Key Takeaways:1. Operating systems beat tool stacks - Stop logging into 20 different UIs. Build one central interface through Cursor and Claude Desktop that connects to everything. The composable mindset adapts to your needs.2. MCP changes PM workflows forever - Model Context Protocol lets you connect JIRA, Figma, GitHub, Notion, Confluence through natural language. Check ticket status without opening JIRA. Compare designs without manual cross-reference.3. Design validation takes 30 seconds now - "Find my Confluence doc about Feature X, load this Figma design, compare them and tell me what I missed." Used to take 1-2 hours of manual comparison work.4. Manus dominates heavy research - Gives you multiple file outputs: sample CSVs, combined datasets, data sources report, quick start guide, markdown summary. All traceable back to sources. ChatGPT just gives responses.5. Research must stay external until vetted - The "conspiracy theorist LLM" problem is real. If you automatically feed everything into your system, AI anchors to wrong information. Vet research separately, then bring validated context in.6. PMs can build what required engineers - Mike built a colorization app for e-commerce in one morning. Migrated content to Sanity CMS in a few hours. All from natural language prompts in Cursor.7. Context switching kills productivity - Every time you open a new tab, you lose flow state. The operating system keeps you in one interface. The AI handles the context switching for you.8. Corporate IT restrictions become irrelevant - You already have Cursor or Claude Desktop. You already use JIRA, Figma, GitHub. Connect them through a better interface. No new tool approvals needed.9. Analytics workflows save massive time - Export Clarity data, upload to Cursor, prompt "analyze drop-offs and create visualizations." Takes 10 minutes vs hours of manual Excel work.10. AI native PMs think in prompts - "What do I need to do? What are the steps? What tools will help?" Treat AI as an extension of yourself, not a separate tool to learn.----Where to Find Mike* LinkedIn* Youtube* Website----Related ContentNewsletters:* AI Product Strategy* How to Build AI Products* AI Agents for PMs* Product Requirements DocumentsPodcasts:* AI Prototyping for PMs* How to Become an AI PM* Everything You Need to Know About AIPS. Please subscribe on YouTube and follow on Apple & Spotify. It helps! 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 EpisodeChatGPT just made huge waves with its Atlas browser. Perplexity made waves before that with its Comet browser. And Atlassian just spent a billion dollars to buy Dia.Big companies are making big moves in the AI browser space.But should you use an AI browser? Is it safe? Will it make you more effective as a PM?I asked this question at Berkeley last month during my keynote. Out of 500 PMs in the room, literally two hands went up.That needs to change.Naman Pandey has tested these browsers more extensively than anyone else. He runs the Ready Set Do podcast and has spent hundreds of hours finding the real use cases that actually work.Today, we’re putting all three browsers head-to-head. Same prompts. Same tasks. Live demos.You’ll see which browser wins for each use case, where they fall over, and the exact workflows to use them as a PM.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:* Jira Product Discovery: Plan with purpose, ship with confidence* Mobbin: Discover real-world design inspiration* Pendo: The #1 Software Experience Management Platform* Product Faculty: Get $550 off the AI PM Certification with code AAKASH550C7* Land PM job: 12-week experience to master getting a PM job----If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, and Mobbin - for free, grab Aakash’s bundle.Are you searching for a PM job? Join me + 29 others for an intensive 12-week experience to master getting a PM job. Only 14 seats left.----Key Takeaways:1. AI agent browsers are underhyped for PMs - Only 2 out of 500 PMs at Berkeley were using them. If you're doing web research, competitor analysis, or data scraping, you're leaving hours on the table every week.2. The three browsers serve different purposes - ChatGPT Atlas for deep research across multiple pages. Perplexity Comet for real-time quick lookups. Arc Dia for workflow automation. They're not competing head-to-head.3. Atlas dominates data extraction - Scrape YC companies, find recruiters on LinkedIn, build competitor comparison tables. What took 2-3 hours now takes 10 minutes with one prompt.4. Comet wins on speed for real-time info - Stock prices, sports scores, breaking news. It's the fastest by far. Perfect for quick research sprints across Reddit, Twitter, and news sites.5. Dia automates repeated workflows - Monitor competitor pricing weekly. Document onboarding flows. Generate recurring reports. Set it once, let it run on schedule.6. Tab context is the hidden superpower - Open 5 competitor sites. Ask "What's the common pricing strategy?" The AI reads all tabs and synthesizes insights. Eliminates copy-paste friction.7. The job seeker use case is mind-blowing - "Find 20 PMs at Google, get their LinkedIn profiles, draft personalized DMs." Atlas does this in 15 minutes. Used to take 2-3 hours manually.8. Onboarding analysis becomes trivial - "Go through Notion's signup flow, capture screenshots, document each step." Dia does this in 5-10 minutes. Perfect for competitive analysis.9. Don't log into sensitive accounts - Banking, email, social media with private data - keep these in your regular browser. Use AI browsers only for public research and data extraction.10. The slowness matters less than you think - Yes, they're slow compared to Google. But if the alternative is 2 hours of manual work, waiting 10 minutes is a massive win. Batch requests and walk away.----Related ContentNewsletters:* AI Product Strategy* How to Build AI Products* AI Prototyping Tutorial* How to Become an AI PM* Ultimate Guide to OnboardingPodcasts:* How to Build ChatGPT Apps* AI Prototyping for PMs* Everything You Need to Know About AI* AI Product Management Course----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

This is a free preview of a paid episode. To hear more, visit www.news.aakashg.comToday’s EpisodeWhen you first start using AI prototyping tools, you get wowed.You type “create me a CRM application” and boom a fully functioning app appears in 60 seconds.But here’s the problem.It looks generic. The styling is basic. The features are vanilla. You’d never ship this to customers.This is AI slop.Sachin Rekhi was the former Head of Product at LinkedIn Sales Navigator. He’s now teaching thousands of PMs at Reforge how to master AI prototyping.----Brought to you by - Reforge:Get 1 month free of Reforge Build (the AI prototyping tool built for PMs) with code BUILD----Key Takeaways:1. Product shaping changes everything - Anthropic builds multiple prototypes for every problem, launches internally, sees what people use, then productionizes winners. This used to only be possible at Apple with massive labs.2. AI slop is real - Type "create a CRM" and you get generic styling, vanilla features, basic scenarios. Looks magical but you'd never ship it. The challenge is going from slop to production-grade prototypes.3. The 15-skill mastery ladder - Apprentice level: prompting, editing, design consistency. Journeyman: versioning, debugging, diverging. Master: functional prototyping, product shaping, analytics integration.4. Design consistency starts with baselining - Take screenshot of your product. Recreate it. Iterate until perfect. Save as template. Now every prototype inherits your design system automatically.5. Diverging is the secret weapon - Generate 4 design variants instead of 1. Magic Patterns has this built in. Or use multiple tools to get 8 options. Evaluate alternatives like designers do.6. Functional prototypes unlock real validation - Integrate OpenAI API for actual responses. Add PostHog for session recordings and heatmaps. Build surveys. Track clicks. Test with real data, not mockups.7. The tools face-off: which to actually use - Bolt for speed. V0 for beautiful UIs. Replit for full-stack. Magic Patterns for product teams with diverging. Reforge Build for context integration. Cursor for technical PMs.8. The $5/month unlimited execution hack - Host n8n on Hostinger instead of paying per execution. Get unlimited runs. Build workflow that backs up to Google Drive for version history.9. PMs can build what used to require engineering - Calendar integration. Email agents. Analytics dashboards. Multi-model comparison. Survey collection. All from prompts. No code required.10. Traditional workflows beat agents for production - Workflows save tokens, run faster, and are more reliable. Use agents only when tasks need real decision-making. For known processes, use workflows.----Where to Find Sachin* LinkedIn* Newseltter* X* Youtube* Reforge cources----Related ContentNewsletters:* AI Product Strategy* How to Build AI Products* AI Prototype to Production* AI Prototyping Tutorial* Product Requirements DocumentsPodcasts:* AI Prototyping for PMs* How to Become an AI PM* Everything You Need to Know About AI----PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!If you want to advertise, email productgrowthppp at gmail.

This is a free preview of a paid episode. To hear more, visit www.news.aakashg.comToday’s EpisodeChatGPT Apps might be the next billion-dollar opportunity.Or they might be another ChatGPT feature that gets abandoned in 6 months.I genuinely don’t know yet.But when people say “this could be the new App Store,” my ears perk up. I spent four years building an iOS app in the early days of the App Store. The distribution was incredible. We grew fast purely because of where we were.So when OpenAI announced the ChatGPT App Store, I needed to understand it.I brought in Colin Matthews to break it down. Colin is one of my go-to sources for technical product topics. Our AI prototyping collaborations have been some of your favorite episodes.Today, we’re exploring ChatGPT Apps and what they mean for you as a product builder.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:* Maven: Get $500 off with my code on Coil Build ChatGPT Apps course* Vanta: Automate compliance, Get $1,000 with my link* Land PM job: 12-week experience to master getting a PM job* Mobbin: Discover real-world design inspiration* NayaOne: Airgapped cloud-agnostic sandbox----If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, and Mobbin - for free, grab Aakash’s bundle.Are you searching for a PM job? Join me + 29 others for an intensive 12-week experience to master getting a PM job. Only 23 seats left.----Key Takeaways:1. ChatGPT apps = MCP + widgets - The Model Context Protocol (invented by Anthropic) lets AI agents call external tools. OpenAI added UI widgets on top to create embedded app experiences directly in chat.2. 900M weekly active users = massive distribution opportunity - This is the new SEO. Early data shows 26% higher conversion from AI traffic vs traditional search. Every enterprise will eventually build here.3. You're building for multiple platforms - MCP works across ChatGPT, Claude (coming soon), Cursor, and other AI tools. Build once, distribute everywhere. Gemini doesn't support it yet.4. Apps get called based on tool descriptions - Your metadata matters. Like SEO but for LLMs. Run evals to test if correct prompts trigger your tools. Iterate on descriptions to improve discovery.5. Three eval categories: direct, indirect, negative - Direct: user names your app. Indirect: user describes outcome. Negative: irrelevant request shouldn't trigger your tool. Test all three systematically.6. PMs should prototype but engineers ship production - Use tools like Chippy to prototype quickly and test concepts. Show stakeholders real interactions. Engineering team builds the production version.7. Enterprise-first, solo builders second - Large companies (Target, Uber, Canva) are early adopters chasing distribution. But huge opportunity for indie builders once public marketplace launches.8. Best opportunities: embedded collaboration tools - Spreadsheets, task lists, whiteboards where ChatGPT can partner with you. Not just search results—actual interactive experiences.9. Error analysis on observability logs is critical - Track what prompts triggered which tools with what parameters. Look for mismatches between expected and actual behavior. Iterate tool descriptions.10. Marketplace launching by end of 2024/early 2025 - Currently only launch partners can publish. Public marketplace coming soon means anyone can ship apps and reach ChatGPT's massive user base.----Where to Find Colin* LinkedIn* NewsletterRelated ContentNewsletters:* AI Prototyping Tutorial* How to Build AI Products* AI Product Strategy* Complete Course: AI Product ManagementPodcasts:* AI Prototyping for PMs* How to Become an AI PM* Everything You Need to Know About AI----PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!If you want to advertise, email productgrowthppp at gmail.

Today’s EpisodeEveryone’s demoing AI features. Few are shipping them to production reliably.The gap? Evals.Not the theoretical kind. The real-world kind that catches bugs before users do.Hamel Husain and Shreya Shankar train people at OpenAI, Anthropic, Google, and Meta on how to build AI products that actually work. Their Maven course is the top-grossing course on the platform.Today, they’re walking you through their complete eval process.----Brought to you by:* The AI Evals Course for PMs & Engineers: You get $800 with this link* Vanta: Automate compliance, Get $1,000 with my link* Jira Product Discovery: Plan with purpose, ship with confidence* Land PM job: 12-week experience to master getting a PM job* Pendo: the #1 Software Experience Management Platform----If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, and Mobbin - for free, grab Aakash’s bundle.Are you searching for a PM job? Join me + 29 others for an intensive 12-week experience to master getting a PM job. Only 23 seats left.----Key Takeaways:1. AI evals are the #1 most important new skill for PMs in 2025 - Even Claude Code teams do evals upstream. For custom applications, systematic evaluation is non-negotiable. Dog fooding alone isn't enough at scale.2. Error analysis is the secret weapon most teams skip - Looking at 100 traces teaches you more than any generic metric. Hamel: "If you try to use helpfulness scores, the LLM won't catch the real product issues."3. Use observability tools but don't depend on them completely - Brain Trust, LangSmith, Arise all work. But Shreya and Hamel teach students to vibe code their own trace viewers. Sometimes CSV files are enough to start.4. Never use agreement as your eval metric - It's a trap. A judge that always says "pass" can have 90% accuracy if failures are rare. Use TPR (true positive rate) and TNR (true negative rate) instead.5. Open coding then axial coding reveals patterns - Write notes on 100 traces without root cause analysis. Then categorize into 5-6 actionable themes. Use LLMs to help but refine manually.6. Product managers must do the error analysis themselves - Don't outsource to developers. Engineers lack domain context. Hamel: "It's almost a tragedy to separate the prompt from the product manager because it's English."7. Real traces reveal what demos hide - Chat GPT said the assistant was correct but missed: wrong bathroom configuration, markdown in SMS, double-booked tours, ignored handoff requests.8. Binary scores beat 1-5 scales for LLM judges - Easier to validate alignment. Business decisions are binary anyway. LLMs struggle with nuanced numerical scoring.9. Code-based evals for formatting, LLM judges for subjective calls - Markdown in text messages? Write a simple assertion. Human handoff quality? Need an LLM judge with proper rubric.10. Start with traces even before launch - Dog food your own app. Recruit friends as beta testers. Generate synthetic inputs only as last resort. Error analysis works best with real user behavior.----Where To Find Them* LinkedIn:* Hamel: Hamel’s LinkedIn* Shreya: Shreya’s LinkedIn* AI Evals Course: World’s best AI Evals Course (You get $800 off with this link)Related ContentNewsletters:* AI Evals* AI PM Observability* AI Testing* LLM Judge* AI Prototype to Production* AI Product Strategy* How to Build AI Products* Prompt EngineeringPodcasts:* AI Evals: Everything You Need to Know to Start* Everything you need to know about AI (for PMs and builders)* Carl Vellotti on Claude Code* Marily Nika on Google AI PM Tool Stack* Pawel Huryn on n8n for PMsPS. Please subscribe on YouTube and follow on Apple & Spotify. It helps! 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 EpisodeCarl Vellotti is back.His first episode hit 30k views. My most popular episode in months.Today, he’s giving you the advanced playbook for Claude Code.Claude Code hit $1 billion ARR in December. Fastest product ever to reach that milestone. Under 6 months.Carl has been building with Claude Code every single day. He’s made all the mistakes. He’s learned all the expert workflows and tips.In today’s episode, he shares everything.----If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, and Mobbin - for free, grab Aakash’s bundle.Are you searching for a PM job? Join me + 29 others for an intensive 12-week experience to master getting a PM job.----Brought to you by:Amplitude: The market-leader in product analyticsPendo: the #1 Software Experience Management PlatformJira Product Discovery: Move discovery and roadmapping out of spreadsheetsMiro: The innovation workspace----Key Takeaways1. Claude Code hit $1B ARR in 6 months - fastest product ever. Anthropic optimized for power users doing deep work. Fewer users, insanely deep usage. Carl: "I spend all day in Claude Code. It gets to the point where you never really have to leave."2. MCPs connect Claude to everything in 2 minutes - Model Context Protocol invented by Anthropic. One command to install Linear, Google Workspace, Slack. Essential stack: documents first, task management second, communication third, data sources fourth.3. End-to-end PM workflow takes one morning instead of one week - Survey creation → analysis → PRD → presentation → 19 tickets. All through MCPs. Carl didn't use templates. Opus 4.5 handled everything.4. Skills are pre-built templates but don't always auto-trigger yet - Solution: specify explicitly. "Create presentation using your presentation skill." Build Skills for anything you do more than twice. That's the compound effect.5. Real Google Slides, not images - Carl's presentation skill created 19 fully editable slides from PRD. Takes a few minutes because Claude creates slides in parallel. "This is saving you hours. We used to spend entire afternoons turning PRDs into decks."6. GitHub turns Claude into remote worker - Create issues from your phone. @mention Claude. It works while you're away. Carl's journal is a private GitHub repo. Voice transcriptions → issues → Claude updates markdown.7. Workflows beat agents for production - Level 1 workflow: 5,000 tokens, 40 seconds. Level 3 agent: 90,000 tokens, 90 seconds. Carl's rule: "If something can be expressed as code, code it. Leave agents for cognitive work."8. Production best practices separate hobbyists from pros - Set error workflows. Add retry logic (3 attempts, 1-second delays). Pin data during development. Use reasoning effort appropriately. Write detailed tool descriptions.9. Real challenge is building reusable workflows - Learning basics takes a day. First workflow takes hours. But having 50 workflows built over months means everything is automated. Carl: "Because I have workflows for all these things already built, I can do it all in one shot."10. Tools improve faster than you can learn them - Carl: "By the time you see this, there might be Claude 5." Your workflows from today will 10x when better models drop. PMs who started 6 months ago aren't just 6 months ahead, they're 100x ahead because systems compound with every improvement.----Where to Find Carl Vellotti* Linkedin* X (Twitter)* Instagram* NewsletterRelated ContentNewsletters:* How to Use Claude Code Like a Pro* Steal 6 of My Claude Skills* Claude Skills Tutorial* AI Agents: The Ultimate Guide for PMs* Practical AI AgentsPodcasts:* The Ultimate Guide to n8n for PMs, with Pawel Huryn* This is what a Google AI PM’s Tool Stack Looks Like, with Marily Nika* How to Use Claude Code Like a Pro (Carl’s First Episode)----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

Marily Nika and I filmed my 7th most popular episode a year ago.She broke down everything on how to become an AIPM because she’s done it. She is an AIPM at Google with 11 years of experience.Today, she’s back with a completely new episode. She’s dropping every AI tool she uses daily as an AIPM.Not theory. Not hype. The actual 6 tools she uses multiple times every single day.Today she’s dropping all the expert tips and tricks. She’s made the mistakes so you don’t have to.----Today’s guide covers:* The AI PM Tool Stack* Google AI Studio for Prototyping* Opal for Mini Apps* Notebook LM for Domain Expertise* Perplexity for Reddit Research* ChatGPT for Your Voice* Fireflies and Getting Tools at Work* The 18-Month AIPM Roadmap* The AIPM Interview Red Flags----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:* Jira Product Discovery: Move discovery and roadmapping out of spreadsheets* Vanta: Automate compliance across 35+ frameworks* Maven: Improve your PM skills with awesome courses. Discount with my link* Mobbin: Discover real-world design inspiration* Product Faculty: Get $500 off the AI PM Certification with code AAKASH25----Key Takeaways1. Marily's stack is just 6 tools - Google AI Studio for prototyping, Opal for mini apps, Notebook LM for learning, Perplexity for user research, ChatGPT for PRDs, and Fireflies for meetings. Each maps to one PM workflow. No tool hopping.2. Prototype before writing PRDs. Marily builds a working app in AI Studio first, then brings engineers in to debate the actual thing. Saves weeks of back-and-forth on documents. PRDs are now for complex cross-functional work only.3. Notebook LM learned a 4-hour video in 15 minutes. Marily had an interview the next day. She uploaded the job description and a 4-hour investor relations video. It gave her 15 key points. She memorized them and crushed the interview.4. Perplexity's Reddit filter is your secret weapon. Turn off web search, turn on discussions and opinions. Ask "would young professionals be interested in a fitness ring?" Get 20+ Reddit sources instantly. Know what users actually want.5. Marily's PRD generator has 10,000+ users. She trained a ChatGPT custom GPT with her voice and old PRDs. It asks probing questions before generating. Gets PRDs done in days instead of weeks. She's not embarrassed to use AI at work.6. Tool selection has 4 rules. Does it save 10x time (not 2x)? Does it work across contexts? Does it work with your company's limitations? Does it compound over time? If not all 4, delete it.7. "Be like a crab" for AIPM roles. Move adjacent to your experience. Hearing aids → AirPods PM. ESPN journalism → Meta sports AI PM. Don't ignore your past experience. It's your competitive advantage.8. Red flag in AIPM interviews - missing PM craft. Ex-ML scientists dive into algorithms without asking why, who, and how to measure success. AI changes HOW you fulfill use cases, not the use cases themselves.9. Who replaces PMs? Not AI. PMs who use AI when you're not. Don't be embarrassed about using AI at work. It's not cheating. The gap is widening between PMs who use these tools and those who don't.10. Every PM will become an AIPM by 2026. Show Marily any product and she'll find the AI use case. Even retail stores use cameras and AI to monitor which areas get traffic. The AIPM title will just become PM.----Where to Find Marily* LinkedIn* Twitter* Website* YouTube* Newsletter* Maven - Her course is part of my special curation with Maven----Related ContentNewsletters* AI Foundations for PMs* How to Become an AIPM* AI Prototyping for PMs* AI PRDs* The Complete AI PM Course* AI Product Strategy* ChatGPT for PMsPodcasts* Pawel Huryn on n8n* Alex Danilowicz on AI Prototyping* Hamza Farooq on Landing AIPM Jobs* Todd Olson on Upskilling to AIPM* Rachel Wolan on AI Product Leadership----If you want access to AI tools for free, get Aakash’s bundle and access to 9 AI products for an entire year.Are you searching for a PM job? Join me + 29 others for an intensive 12-week experience to master getting a PM job.----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

This is a free preview of a paid episode. To hear more, visit www.news.aakashg.comPawel Huryn is the guest behind my most popular episode ever (52K+ views).Today, he’s back to give you a masterclass in one of the most exciting AI tools out there: n8n.n8n is the most powerful workflow automation tool that combines two things: traditional workflow automation and building AI agents.And Pawel has been knee-deep in n8n more than almost anyone else in the world.He’s tried everything. He’s made all the mistakes. He’s learned all the expert workflows and tips and tricks.In today’s episode, he walks through building real n8n workflows from scratch.-----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:* Amplitude: The market-leader in product analytics* Vanta: Automate compliance across 35+ frameworks* Testkube: Leading test orchestration platform* Kameleoon: AI experimentation platform* Pendo: The #1 software experience management platform-----Key Takeaways1. N8N combines traditional workflow automation AND AI agent building in one platform - making it more powerful than Zapier or Make for complex automation needs.2. Real use cases span from simple business workflows to chatbots, automatic competitor monitoring, multi-agent research systems, and inbox workers that take actions based on emails. Sky is the limit.3. Pavel's competitor monitoring workflow costs $1-2/week using the FREE version of N8N. Just needs Perplexity API ($1-2 for hundreds of calls) and OpenAI credits. Enterprise tools charge $500+/month.4. Pin your data during development. N8N caches API responses so you don't burn credits while testing workflows. Click the pin icon and N8N uses cached data instead of making new API calls.5. N8N automatically loops through items - no need to write for-loops or while-loops. When you connect a node with 6 items, N8N repeats the action 6 times automatically.6. Compress context before sending to LLMs. Pavel cuts 70% of tokens by extracting only summary content and citation URLs from Perplexity results, ignoring irrelevant snippets and metadata.7. Use ChatGPT to write N8N code snippets. Pavel never writes code blocks himself - just takes a screenshot of the data and asks GPT "how do I compress this information?"8. Traditional workflows are more efficient (saves tokens, very reliable) for predictable tasks. AI agents are more flexible but use more tokens and can make mistakes. Use workflows when you know the steps.9. Set GPT reasoning effort to "low" for simple tasks. When you just need formatting or summarization (not complex thinking), low reasoning effort saves tokens significantly.10. Best practices: Set dedicated error probes to catch errors before they break workflows. Use max iterations to prevent infinite loops. Set retry on fail to 3x attempts. Pin data during development.-----Where to Find Pawel* LinkedIn* Newsletter* YouTube-----Related ContentNewsletters:* Ultimate Guide to AI Prototyping Tools* AI Agents: The Ultimate Guide for PMs* Practical AI AgentsPodcasts:* AI Agents Demo, with CEO of Relay.app* How to Build AI Agents (and Get Paid $750K+)* 5 AI Agents Every PM Should Build, with CEO of LindyIf you want access to a n8n competitor for free, get Aakash’s bundle and access to Relay.app.-----If you want to advertise, email productgrowthppp at gmail.

Today’s EpisodeLaura Burkhauser started as an IC PM at Descript, the $550M AI video editing platform.Three years later, she’s CEO.She shipped AI features that worked.In today’s episode, you’ll hear the exact features Laura built, the eval framework she used, and her complete IC → CEO path.This might be the most actionable career episode you listen to all year.----Check out the conversation on Apple, Spotify and YouTube.Brought to you by:* Maven: Improve your PM skills with awesome courses* Pendo: #1 Software Experience Management Platform* Vanta: Automate compliance across 35+ frameworks like SOC 2 and ISO 27001* NayaOne: Airgapped cloud-agnostic sandbox* Kameleoon: Leading AI experimentation platform----Key Takeaways1. Map your user journey BEFORE picking AI features. DeScript identified pain points (retakes, eye contact, rambling), then asked "what just became possible with LLMs?" Build that intersection.2. Build prepackaged buttons, not blank chat boxes. Each DeScript AI tool is a carefully crafted prompt behind a single button that delivers reliable results every time.3. Use human evals on production data before shipping. Test on real customer data, ask "would I use this as a customer?" If yes, ship. If no, don't.4. The ultimate metric is export rate. If users apply your AI feature then remove it before exporting, it didn't meet their quality bar.5. Switch from buttons to chat when you hit 30+ parameters. When users wanted topic selection, speaker choice, and platform optimization, chat became better than buttons.6. Match your eval data to actual use case. DeScript failed with Studio Sound because they tested on terrible audio (vacuuming, jackhammers) when real users had laptop microphones. Different models handle different quality levels.7. Test agents with real customer language early. Don't use toy data or employee terminology. Mix sophistication levels—some advanced at video and AI, some complete beginners—to understand how real people prompt.8. Launch AI agents to new users first. Video editing is hard and many people quit. DeScript tested Underlord on activation and it won, so new users got it first before existing users.9. Choose breadth over depth for product-wide agents. DeScript chose breadth—Underlord works across all features because "we're not a point solution." Requires more context, tool coverage, and evals but serves the product vision.10. Earn founder trust by getting command, not by being strategic. Use the product extensively. Talk to customers constantly. When you speak, people think "Smart" and invite you to more rooms. Ship features before focusing on strategy.----Where to Find Laura Burkhauser* LinkedIn* Company----Related ContentPodcasts:* AI Product Leadership Masterclass* Conversation with the CEO and Founder of Bolt* This $20M AI Founder Is Challenging Elon and Sam Altman | Roy Lee, CluelyNewsletters:* Ultimate Guide to AI Prototyping Tools* AI Agents: The Ultimate Guide for PMs* How to Land a $300K+ AI Product Manager JobPS. Please subscribe on YouTube and follow on Apple & Spotify. It helps! 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

Check out the conversation on Apple, Spotify and YouTube.Brought to you by:* Jira Product Discovery: Move discovery and roadmapping out of spreadsheets* Vanta: Automate compliance across 35+ frameworks like SOC 2 and ISO 27001* Kameleoon: Leading AI experimentation platform* NayaOne: Airgapped cloud-agnostic sandbox* Product Faculty: Get $500 off the AI PM Certification with code AAKASH25Today’s EpisodeClaude Code hit $1B ARR in 6 months. But OpenAI is not just giving up.ChatGPT’s new Codex is the most powerful way for product managers to build prototypes. And it’s a far better way to use ChatGPT than the browser.So every PM should know how to use it.Today, I brought back the man behind my Claude Code tutorial, Carl Vellotti, for a full guide on how to use ChatGPT Codex for PMs:This might be the most important podcast you watch all year. (And not a single other PM podcast has even talked about this tool)Your Newsletter Subscriber BonusFor subscribers, each episode I also write up a newsletter version of the podcast. Thank you for having me in your inbox.Today’s guide covers:* Getting Started: The Non-Technical PM’s Guide to Codex CLI* Classic PM Tasks: Documents, Meetings & PRDs with Codex* Advanced Prototyping: From Vibe Coder to Vibe EngineerThis is your complete Codex roadmap.1. How to Get Started - The Non-Technical PM’s Guide to Codex CLIIf you’ve never touched a terminal, this is for you.1a. Open Codex in an IDECarl opens Cursor and shows files Codex creates in real-time.Workflow: Open project folder in Cursor → Terminal (Control + backtick) → Type codex.Now you see files created, preview documents, navigate visually.As Carl Says:“Open it in an IDE. Easiest way to see what it’s actually doing.”1b. The YOLO Mode HackCodex asks for permission constantly—every website, every command.Solution:codex --yoloFull access mode. No prompts. Just execution.“Haven’t broken my computer yet. We’re in this directory so it won’t leave.”1c. Codex vs Claude CodeSame task on both: Search web, summarize differences.Claude Code: 3 searches simultaneously, ~2 minCodex: One site at a time, asks permissions, ~4 minClaude Code is faster, hides details. Codex shows every command—more verbose.“It’s Apple versus Microsoft. Claude does things in a nicer interface. Codex shows you everything.”2. How to Handle Classic PM Tasks with CodexCodex shines at daily PM work.2a. File Analysis Without UploadingRun Codex from a folder, it accesses everything. No uploading.Carl has demo folder (Taskflow) with interviews, notes, PRDs.“What user interviews completed?” → Codex lists them“Top 3 pain points?” → Returns: voice input reliability, integration gaps, template workflowCreates document with direct quotes—no manual file providing.2b. Template-Driven WorkflowsSolution: Create /templates folder with markdown files.Example: Discussion Points, Action Items, Risks & Blockers, Next StepsUse: Summarize @meeting-notes using @templateSame format every time. Works for PRDs, one-pagers, research summaries.2c. Socratic Questioning for Better PRDsProblem: Asking Codex to write a PRD produces garbage without proper thinking.Solution: Socratic template makes Codex ask YOU questions first.Questions: “Why is this helpful? Data, feedback, or strategic?” “What must work for V1?” “Edge cases?”You answer. AI embeds your thinking.Then Codex reviews context, templates, example PRDs, and writes.Carl: “Goes from kind of there to a really good PRD almost right out of the box.”3. Advanced Codex Techniques for Future Vibe EngineersThis is where Codex separates from the browser.3a. Design Systems with StorybookVibe coder problem: “Make title pink” → wrong shade → 20 iterations → still jankyVibe engineer solution: Storybook (npm run storybook)See components visually, change colors instantly, see changes live without redeploying.Carl changes recipe title to pink: read-only mode to see plan, then YOLO mode to execute. Updates in Storybook immediately.Pre-built components: Use Shadcn UI for production-grade React components. Calendar, date picker, dropdown—all done.“All the logic and difficult things, you get for free.”3b. Test-Driven Development is The UnlockProblem: AI says “Done!” but it’s broken. AI will lie and set variables to true to pass tests.Solution: Write tests BEFORE building.Red-Green-Refactor: Tests fail → Build until pass → Improve codeCarl’s macro calculator:Tests: API returns calories, empty recipe returns null, missing data shows “N/A”, API fails retries 3x, divide by servings correctlyTold Codex: “Build to make tests pass.”35 minutes. Zero touch. All tests green. Feature worked.Carl: “If you write tests FIRST, they can’t cheat.”3c. An Example - The TikTok Recipe BotCarl built this for his girlfriend: TikTok recipe extractor.Problem: Recipe videos on TikTok. No written recipe. Just “comment and I’ll DM.”Solution: Paste URL → Codex downloads video → Sends to Gemini (only model that processes video) → Gemini extracts recipe → Codex formats to PDFHow: Detailed implementation spec: architecture, data flow, APIs, error handling, retry logic, tests.Gave to Codex in GPT-5 Codex mode. Left 35 minutes.Built entire feature. Tests passed. Worked first try.Carl: “First time I saw the dream—give it a medium-sized feature and it just builds it.”Where to Find Carl Vellotti* Linkedin* X (Twitter)* Instagram* NewsletterRelated ContentPodcasts:* Claude Code Tutorial* Windsurf Tutorial* AI Prototyping TutorialNewsletters:* Ultimate Guide to AI Prototyping Tools* AI Agents: The Ultimate Guide for PMs* How to Land a $300K+ AI Product Manager JobPS. Please subscribe on YouTube and follow on Apple & Spotify. It helps! 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 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

"AI has been the biggest driver of change in experimentation I've seen in my career."That's Frederic De Todaro, Chief Product Officer at Kameleoon (profitable SaaS with 2K+ customers).Fred has been at Kameleoon for 12+ years. In that role, he's helped thousands of teams use AI to experiment faster and smarter.In today’s episode he’s breaking down:How AI changes experimentationHow to experiment with AI featuresLast week, I covered how one aspect of this: vibe experimentation. Today’s video is the A to Z AI impact. If you experiment at work, this episode is for you.----Check out the conversation on Apple, Spotify and YouTube.* Mobbin: Discover real-world design inspiration* Jira Product Discovery: Build the right thing, reliably* AI Product Strategy Certificate for Leaders: Get $550 off----Timestamps:00:00 How AI Changed Experimentation Overview01:54 The 4 Steps of Experimentation Framework14:12 ADS16:00 How AI has Changed Experimentation21:08 User Behaviour Models26:56 Multi-Armed Bandit vs Contextual Bandit30:05 ADS31:55 AI Content Genration35:13 How Vibe Coding Changes Experimentation41:35 Live Demo From Idea to Running Experiment in 2 Minutes43:36 Two-Minute Build Achievement51:49 How to Measure AI Features Properly54:17 Measuring RAG Systems 3 Key Metrics01:07:18 Best Experimentation Company Booking.com01:10:10 Biggest PM Mistakes in Experimentation01:13:52 Ending----Key Takeaways1. Build is the bottleneck. Most teams can't A/B test because developers are busy. AI removes this constraint anyone can now create experiments in minutes using plain English.2. 80% of experiments fail. But with AI opportunity detection, you can drill into failed experiments to find hidden wins, like features that work great on mobile but fail on desktop.3. Vibe coding meets experimentation. It's not enough to build prototypes quickly. You need to test them with real users at scale. Prompt-based experimentation bridges this gap.4. Context is everything. AI performs best when it has access to your website's framework, design system, and past experiments. The more context, the better the ideas and implementations.5. Humans still matter. PMs bring business context, data scientists ensure statistical rigor, and AI handles the grunt work. It's augmentation, not replacement.6. Start simple with feature flags. You don't need to copy Booking.com overnight. Begin with feature flags, then rollouts, then full experimentation. AI makes each step easier.7. Measure beyond usage. For AI features, track: How many prompts to success? Time from idea to live? How often do developers step in? These reveal true value.8. Multi-armed bandits for speed, contextual for personalization. Use multi-armed when you need quick answers. Use contextual when personalizing for each user.9. Discovery and experimentation are partners. Discovery tells you what users say they want. Experimentation tells you what they actually do. You need both for the full picture.----Check out the conversation on Apple, Spotify and YouTube.----Related Podcasts:* How to Build Things Faster as a Product Team* Lessons from Super-Senior IC Experimentation PM* Amplitude CEO: Demo, Story, and How They Build Product-----P.S. More than 85% of you aren't subscribed yet. If you can subscribe on YouTube, follow on Apple & Spotify, my commitment to you is that we'll continue making this content better.----If you want to advertise, email productgrowthppp at gmail. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribe

Check out the conversation on Apple, Spotify and YouTube.Brought to you by:* Jira Product Discovery: Build the right thing* AI PM Certification: Get $550 with code AAKASH550C7* The AI Evals Course for PMs: Get $1050 off with code ag-product-growth* Maven: Get $100 off my curation of their top coursesToday's EpisodeToday’s guest is at the epicenter of AI - and he hasn’t done any podcasts before.In today’s in-person chat, I sit down with Jake Brill, the Head of Integrity Product at OpenAI. He breaks down:* The GPT-5 launch* How OpenAI builds product* What the PM role looks in the the future with AI* The future of building product and the need to build agents into your product* What it takes to break into OpenAIIf you've ever wondered what it takes to work at OpenAI or how to build AI products at scale, this episode is for you.Your Newsletter Subscriber Bonus:For subscribers, each episode I also write up a newsletter version of the podcast. Thank you for having me in your inbox.(By the way, we’ve launched our podcast clips channel as well and we’re going to post most valuable podcast moments on this channel, so don’t miss out: subscribe here.)1. Integrity’s Role in the GPT-5 LaunchMost PMs think about launching products in terms of features and marketing. But when you're serving hundreds of millions of users with breakthrough AI, the real challenge is infrastructure that can handle the surge without breaking.OpenAI’s integrity team played 3 roles in GPT-5’s launch.Pillar 1 - Identity SystemsIdentity systems must scale from normal traffic to potentially 10x volume overnight. The technical challenge involves load balancing, database scaling, and ensuring your signup flow doesn't crash when everyone hits "Create Account" simultaneously.Pillar 2 - Financial InfrastructureFinancial systems need bulletproof payment processing and fraud detection as conversions spike. This includes sophisticated fraud prevention - bad actors specifically target new model launches to exploit capabilities with stolen credit cards.Pillar 3 - Safety SystemsSafety systems require multiple defense layers: model training, input/output classifiers, and behavioral monitoring. Red teaming happens during model training, at production checkpoints, and continuously post-launch.What most PMs miss is that integrity isn't just defensive - it's an enabler of scale. Without rock-solid integrity infrastructure, even the most advanced AI models can't reach their intended audience.Do you need an integrity team? If you're building consumer AI products at scale, handling sensitive data, or processing payments, the answer is probably yes.2. How OpenAI Builds ProductOpenAI operates with a unique product philosophy that breaks traditional PM playbooks. While most companies start with user problems and build solutions, OpenAI often starts with breakthrough capabilities and figures out how to bring them to humanity.Let’s zoom in on 5 key takeways about how they build product: Takeaway 1 - The Research-First ApproachJake describes this inverted approach: "We've got the best researchers in the world building the most powerful AI capabilities in the world. And sometimes it's like, holy moly, we just had this big research breakthrough. How do we bring this capability to humanity?"This research-first methodology requires unprecedented collaboration between product and research teams from day one, not as an afterthought.Takeaway 2 - Planning That Embraces UncertaintyTheir planning process intentionally embraces uncertainty. Teams plan quarterly but assume only 60-70% completion rates."If you do anything more than that, it probably means you weren't being flexible enough to the needs of the business. If you do anything less than that, probably didn't do a great job forecasting."Plans are written in pencil, not pen, with lightweight documents and async reviews wherever possible.Takeaway 3 - Product Reviews Stay Startup-StyleProduct reviews maintain startup-style directness despite OpenAI's scale. "People come in, it doesn't matter what level you are, you can talk directly with leadership. You don't have to have a fancy slide deck."This creates trust through transparency and hiring excellence rather than process overhead.Takeaway 4 - Heavy Slack CultureOpenAI runs almost entirely on Slack. Jake estimates "conservatively like 90% of my written communication is in Slack." They've built AI agents directly into their Slack channels for Q&A and operational tasks.Takeaway 5 - Iterative Deployment PhilosophyThe company's belief in iterative deployment shapes how they handle uncertainty. Rather than trying to predict every possible misuse case, they identify non-negotiable risks to mitigate before launch, build monitoring systems for edge cases, and "very quickly respond and build sophisticated solutions" based on real-world usage patterns."Actually, at the end of the day, it's really helpful to follow OpenAI's approach of iterative deployment, because once you start rolling things out, you can actually see in the real world how people accidentally might misuse your products."3. What the PM Role Looks Like in the AI FutureThree major shifts define the future PM, Jake's perspective from the epicenter of AI development reveals:Shift 1 - From Specification Writer to Evaluation ArchitectThe PM role is fundamentally shifting from specification writer to AI evaluation architect. Jake's team increasingly asks PMs to write evals because "they have the clearest vision of how the product should work in their head."The evaluation writing skill becomes critical as AI products require objective measurement frameworks. This differs from traditional product metrics by focusing on capability assessment rather than just usage measurement.Shift 2 - AI Prototyping Replaces Lengthy SpecsAI prototyping specifically transforms how PMs communicate vision. Instead of lengthy written specifications, PMs can now build functional demonstrations. "Rather than just writing a proposal for how something works, just build a prototype of how something could work and you put that in people's hands."This shift from description to demonstration accelerates feedback cycles and reduces interpretation gaps between teams.Traditional PRDs still matter, but they're becoming AI-enhanced and less wordy. "I think they're gonna go hand in hand the prototypes and the PRDs. I do think PRDs will be less wordy because you won't have to spend as much time describing oh you click on this button and this thing happens you can just show people."Shift 3 - The Human Elements Become More ImportantBut the human elements intensify rather than diminish. Jake emphasizes that empathy remains the most critical PM skill: "Fundamentally, you are building products for people who are nothing like you. They may live in a different part of the world. They may be a different age, different gender."In five years, Jake predicts PMs will need to manage not just humans, but agents.4. The Future of Building Product and the Need to Build Agents Into Your ProductEvery PM needs to think about building AI agents into their products - otherwise they’re missing out on the future of product. Why Agents MatterJake frames this transition clearly: "For those first couple of years, it's really been what we call assistance. You asked a model a question, you give it a prompt and you get a response…But where we foresee this technology going is not just question and answer, but rather, here's a task. Can you please complete it for me?"This transition requires rethinking product architecture at a fundamental level. Most digital products today are synchronous - "I take an action and a response or something else happens immediately."Agent-first products embrace asynchronous complexity where "someone clicks button and something far more complex can happen behind the scenes and you don't have to sit there waiting for response."Real-World Agent ImplementationJake already demonstrates this shift in his daily work. He uses agents for recruiting ("here's sort of the properties. Ideally, they have X years of design experience... Please go help and source some candidates"), medical research, and market analysis.For PMs specifically, agents excel at competitive analysis, presentation creation, and prototyping - areas where the combination of research depth and creative output provides immediate value.The strategic imperative is clear: "If you're not thinking about how to build products that are agentic in their fundamental nature, you're probably A, not maximizing the power of this technology and B, you're probably building a product that's going to be obsolete in a shorter time horizon."The Infrastructure ChallengeThe challenge extends beyond individual products to ecosystem integration. As Jake notes, "there's not going to be just one company building agentic products" and "the failure state would be if there's not a standard language for all of them to talk together."Products need to consider how their agents will communicate with other agents, requiring standards like MCP (Model Context Protocol) for tool integration and future protocols for agent-to-agent communication.Companies building agent-first products must also prepare for agent reliability challenges. Jake discusses the emerging problem of deceptive behavior: "agents learning to cheat" requires multiple defense layers including alignment training, behavioral monitoring, and constant red teaming.The solution involves "model training, model level classifiers, actor level classifiers, production monitoring, and then just like constant red teaming."5. What It Takes to Break Into OpenAIWhen I asked my newsletter subscribers for their dream company, OpenAI was the overwhelming #1 dream company 600 first-place votes compared to 200 for second place.Jake's advice for getting into OpenAI?* Start Building with AI: You really need to have a facility with AI products and models. There's no excuse for not—it's so easy to use these products, to vibe code, to play around with APIs.* Get a Referral: When we post jobs publicly, a lot of people apply. It really helps if someone at the company has worked with you and can speak to your skill.* Show Relevant Experience: Make your AI experience and domain expertise explicitly clear on your resume and LinkedIn.* Broad Background Wins: The best candidates aren't just specialists. They have experience across different areas (fraud AND ads AND growth) giving them multiple perspectives on ambiguous problems.Jake's own story is proof: he spent 2 months preparing while on paternity leave, treating the interview process with the intensity it deserved because "it felt like what I'd been working towards my whole career."The full conversation covers Jake's journey from Facebook's first internal PM hire to surviving a traumatic brain injury to leading integrity at the world's most important AI company. Essential listening if you're building your career in AI products. Watch or listen now. Key TakeawaysWhere to find Jake* LinkedInRelated ContentPodcasts:* Complete Course: AI Product Management* 5 AI Agents Every PM Should Build* Full Roadmap: Become an AI PMNewsletters:* How to become an AI Product Manager* AI Agents: The Ultimate Guide for PMs* The AI PM's Playbook: How Top PMs Are 10x-ing Their ImpactP.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. 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

If you’ve ever said “I just wish I had an assistant who knew exactly how I think”... Lindy is that assistant. These agents aren’t demos. They’re real, customizable workflows anyone can build. No code.Flo Crivello (founder of Lindy and ex-Cruise/YC) joined us to show how his personal AI stack runs his entire workday: From triaging emails, summarizing meetings, blocking spam, managing contacts, and even sourcing candidates.We’re not talking theory here. You’ll see what’s possible today (no prompting skills, no code), just real agents doing real work when you give them instructions in plain English Language.If you’ve ever wondered: “What can AI actually do for me right now?”This episode answers it - line by line, screen by screen.----Brought to you by:Mobbin: Discover real-world design inspirationJira Product Discovery: Build the right thingProduct Faculty: #1 AI PM Certification (Class Starts: 15 Sep, get $500 off)----Timestamps:AI Agents Can Replace Your Team - 00:00:00The Top 5 AI Agents Every Entrepreneur Needs - 00:03:13The Golden Framework: When to Build an Agent - 00:08:50Keeping AI Agents Safe Without Killing Innovation - 00:09:42From Doer to Orchestrator: The New Management Mindset - 00:11:08Lindy vs ChatGPT: Individual Tools vs Work Platforms - 00:13:23Managing Agent Performance, Permissions & Costs - 00:14:26AD: Mobbin - 00:15:56AD: Jira Product Discovery - 00:16:55Managing Context and Token Costs - 00:17:51Lindy vs Competitors and Zapier - 00:18:58Inside Lindy's 5x Growth in 6 Months - 00:20:38The Salesperson Managing 40 AI Employees - 00:21:45The Soham Scandal: Hiring the 5-Job Engineer - 00:23:20From PM to AI Visionary: The Founding Story - 00:28:24AD: Maven - 00:31:21AD: AI PM Certification - 00:32:08The Pivot Philosophy: Action Produces Information - 00:32:54Should Every PM Become an AI Founder? - 00:37:17Talk to Customers, Build Product: Skip Everything Else - 00:38:45The Valley of Death: Surviving the Hardest Pivot - 00:41:02The AI Agent Agency Gold Rush - 00:44:08Big Tech is Missing the AI Agent Revolution - 00:47:42The 10-Year Vision: Fully Autonomous Companies - 00:48:01----Key Takeaways:Takeaways:01. Most startups are stuck in execution mode, when they should be in vision mode. Instead of spending 80% of time shipping and 20% dreaming, it should be the reverse. The best early-stage companies obsess over the product like artists—not analysts. That’s how you create something magnetic from day one. Not with OKRs. With taste.2. AI agents aren’t just features, they’re teammates. The real unlock with agents is that they collaborate like coworkers. You don’t “use” them, you talk to them. You delegate. They respond. They improve. And suddenly you’re not just automating—you’re offloading real cognitive work.3. You don’t build AI, you build trust. That’s the actual job. Trust is earned slowly: confirmation prompts, human-in-the-loop controls, clear audit trails. Give users the steering wheel first… and gradually ease them into self-driving. That’s how agents move from novelty to necessity.4. The smaller the scope, the sharper the tool. Most teams overbuild. The smarter play is to shrink the surface area until there’s zero ambiguity. That’s when you get magic: crisp execution, no confusion, and fast iteration. Think razor blade, not Swiss Army knife.5. From systems thinking to obsessive craft. Coming from Uber taught how to reason through complex marketplaces. But what unlocked breakthrough product thinking was obsessing over the UX like an artist—not just designing for logic, but for love.6. The future of work is voice → delegation → done.You won’t click through dashboards or jump tabs. You’ll just say, “Handle my recruiting outreach,” and it’ll happen. Behind the scenes: agents coordinating tasks, tracking progress, personalizing copy. And all you did was ask.7. Write Like Your Career Depends on It, Because It Does. Clarity of thought = clarity of writing. He said the best PMs he’s worked with are excellent writers. Not because it looks good, but because it reflects structured thinking.8. Product Sense Is a Muscle. He builds product by imagining it from the user’s emotional POV. Not “What features should we ship?” but “What would delight the user in this moment?”9. You Can't Delegate Taste. No matter how senior you are, if you're not involved in the details of product quality, you’ll lose the magic. He reviews designs himself, edits copy, and obsesses over UX, because product taste is not outsourceable.10. Go Where Product Is Sacred. A PM’s growth is tied to the culture. He picked Uber because product rigor was high. At Lindy, he made product obsession part of the DNA. If your company doesn’t value product deeply, leave.----Check out the conversation on Apple, Spotify and YouTube.----Related Podcasts:We Built an AI Employee in 62 mins (Cursor, ChatGPT, Gibson, Crew AI)Bolt Tutorial from the CEOThis $20M AI Founder Is Challenging Elon and Sam Altman | Roy Lee, Cluely----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

In today’s episode, we have one of the two voices I wanted most when I started this podcast: Teresa Torres. Alongside Marty Cagan, she was in my top guests to have.That’s because she has trained over 17,000 PMs in 100 countries.And in today’s episode, she’s breaking down one of the most important elements of PMing: discovery. She gives a masterclass on how to use the learnings from her smash hit book Continuous Discovery Habits for the AI age, covering both:1. How to do discovery for non-AI features with AI tools2. How to discovery for AI featuresIf you’ve ever wondered why your product ideas sometimes flop, even when the interviews and research looked promising… you’re about to find out why!----Brought to you by:Miro: The innovation workspaceJira Product Discovery: Build the right thingParlance Labs: Practical consulting that improves your AIProduct Faculty: #1 AI PM Certification (Class Starts: 15 Sep, get $500 off)----Timestamps:Teresa's Background - 0:00Story-Based Interviewing - 3:20Fake Discovery Signs - 4:08Assumption Testing - 4:39Continuous Discovery Framework - 5:35AI Changes Discovery - 8:01AI Synthesis Concerns - 9:21AI Prototyping Era - 12:45Ads - 15:45AI Prototyping Workflow - 17:32Common Interview Mistakes - 22:24Interview Synthesis - 24:26OST Updates - 28:53Discovery Theater - 30:52Ads - 32:15Real Product Management - 34:03AI Product Discovery - 35:29Context Engineering - 39:16Orchestration Explained - 42:03Error Analysis - 46:01Observability & Traces - 46:05Claude Code Demo - 49:15Business Numbers - 52:56----Key Takeaways:Takeaways:01. Stop Shipping Blind. Your AI product isn't truly valuable until you validate it. Go beyond just building; understand user needs deeply with personas, journey maps, and jobs-to-be-done.02. MOM Test = Your Secret Weapon. The "MOM Test" is about asking questions that even your most supportive friend can't lie about. Don't ask if users "would" use your AI. Ask about their past behaviors and real problems. This helps you define success metrics and avoid building a fancy toy nobody needs. 03. Evaluate Everything, Relentlessly. AI Evals are not just a technical task for engineers, but the most critical tool for Product Managers to build high-quality, trustworthy AI products. Use them to understand, refine, and continuously improve your AI.04. Passion Won't Land the Job. Proof Will. "I'm passionate"...great I guess, but recruiters want to see what you've done. Your portfolio is your direct line to showing you can actually do the job.05. Build Your AI Portfolio. Now. Don't wait for experience. Create product teardowns of AI tools, develop case studies, or launch small side projects. This is your living, breathing proof of thinking and skill.06. Forget the Resume. Add Value. The ultimate job hack? Identify a problem at a target company and propose a solution, or even build a prototype before you apply. This showcases initiative and concrete skills.07. You’re At Fault (Brutal, I Know). Nailing Prompt Engineering is a direct path to better AI outputs. If your AI misbehaves, it's often your fault for unclear instructions. Refine your prompts for smarter, more reliable AI.08. Generic Resumes In The Bin! Forget sending generic resumes into the void. There are three distinct approaches: just a resume, adding a portfolio and cover letter, or the ultimate "Value Add" where you solve a company's problem before applying. 09. AI Will Do Your Dishes (Metaphorically). While AI Agents promise incredible autonomy and action, remember they still need clear goals and defined tasks. So, while your AI PM dream is big, maybe don't expect it to clean your dishes (yet) – stick to email automation for now!10. Don't Trust LLMs Blindly. LLMs are powerful. But they need continuous human validation and evaluation frameworks. Automate grading where possible, but always, always, have a human in the loop for critical judgment.----Check out the conversation on Apple or Spotify and the demo on YouTube.----Related Podcasts:AI Product Discovery: Complete CourseHow to Do Product Discovery Right with Pawel HurynMarty Cagan on the 4 Key Risks and Importance of DiscoveryHow to Survey and Learn From Your Users with George Harter----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

I made $1M in the last 12 months — with zero full-time employees. In this podcast, I’ll share and show you (watch the YT version) the 7 AI workflows that helped me scale faster, save over $400K in costs, and launch multiple income streams, all powered by AI agents. You’ll see exactly how I use tools to: - Automate my inbox - Build and ship SaaS prototypes - Write and produce video ads - Grow a podcast to 50K+ listeners/episode - Repurpose and distribute content across platforms - Book high-profile guests - Run a content engine with zero human ops----Brought to you by:Miro: The innovation workspace is your team’s new canvasJira Product Discovery: Build the right thingMobbin: Discover real-world design inspirationParlance Labs: Practical consulting that improves your AI----Timestamps:Preview – 00:00:00AI Workflow 1 (Zapier) – 00:00:48AI Workflow 2 (v0) – 00:03:13AI Workflow 3 (Cursor) – 00:08:55AI Workflow 4 (v0.3) – 00:12:04AI Workflow 5 (Lindy) – 00:16:32AI Workflow 6 (Riverside) – 00:19:03AI Workflow 7 (Claude Copilot) – 00:22:47Summarizing Everything – 00:27:52----Check out the conversation on Apple, Spotify and YouTube.----Related Podcasts:This PM Built a Six-Figure ($100K+) AI Side HustleHe Built a $2M/Yr One-Person Business - Steal His PlaybookThis PM was Laid Off - Now he has 125K followersHer Layoff Went Viral - Now She has 300K+ SubscribersHow I make $18K/mo with a niche podcast (STEAL THIS)This Ex Amazon VP Makes $950K In Retirement----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