Today’s Episode

Laura 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.

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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

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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.

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Where to Find Laura Burkhauser

* LinkedIn

* Company

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Related Content

Podcasts:

* AI Product Leadership Masterclass

* Conversation with the CEO and Founder of Bolt

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

Newsletters:

* Ultimate Guide to AI Prototyping Tools

* AI Agents: The Ultimate Guide for PMs

* How to Land a $300K+ AI Product Manager Job

PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!

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 AAKASH25

Today’s Episode

Claude 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 Bonus

For 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 Engineer

This is your complete Codex roadmap.

1. How to Get Started - The Non-Technical PM’s Guide to Codex CLI

If you’ve never touched a terminal, this is for you.

1a. Open Codex in an IDE

Carl 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 Hack

Codex asks for permission constantly—every website, every command.

Solution:

codex --yolo

Full 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 Code

Same task on both: Search web, summarize differences.

Claude Code: 3 searches simultaneously, ~2 minCodex: One site at a time, asks permissions, ~4 min

Claude 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 Codex

Codex shines at daily PM work.

2a. File Analysis Without Uploading

Run 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 workflow

Creates document with direct quotes—no manual file providing.

2b. Template-Driven Workflows

Solution: Create /templates folder with markdown files.

Example: Discussion Points, Action Items, Risks & Blockers, Next Steps

Use: Summarize @meeting-notes using @template

Same format every time. Works for PRDs, one-pagers, research summaries.

2c. Socratic Questioning for Better PRDs

Problem: 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 Engineers

This is where Codex separates from the browser.

3a. Design Systems with Storybook

Vibe coder problem: “Make title pink” → wrong shade → 20 iterations → still janky

Vibe 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 Unlock

Problem: 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 code

Carl’s macro calculator:

Tests: API returns calories, empty recipe returns null, missing data shows “N/A”, API fails retries 3x, divide by servings correctly

Told 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 Bot

Carl 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 PDF

How: 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

* Newsletter

Related Content

Podcasts:

* Claude Code Tutorial

* Windsurf Tutorial

* AI Prototyping Tutorial

Newsletters:

* Ultimate Guide to AI Prototyping Tools

* AI Agents: The Ultimate Guide for PMs

* How to Land a $300K+ AI Product Manager Job

PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!

Today’s Episode

AI 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.

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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

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Key Takeaways

1. 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.

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Where to Find Todd Olson

* LinkedIn

* Company

* X

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Related Content

Podcasts:

* How to Become, and Succeed as, an AI PM | The Marily Nika Episode

* If you only have 2 hrs, this is how to become an AI PM

* Complete Course: AI Product Management

Newsletters:

* How to Become an AI Product Manager with No Experience

* How to Write a Killer AI Product Manager Resume

* How to become an AI Product Manager

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PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!

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If you want to advertise, email productgrowthppp at gmail.

Today’s Episode

There 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.

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Brought to you by:

Linear: The task management platform dethroning Jira

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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.

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Where to Find Rachel Wolan

* LinkedIn

* Website

* X

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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 Lindy

Newsletters:

* AI Evals Guide for PMs

* Prompt Engineering for AI Agents

* AI Agents: The Ultimate Guide for PMs

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PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps

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If you want to advertise, email productgrowthppp at gmail.

Today’s Episode

Why 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:

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Check out the conversation on Apple, Spotify and YouTube.

Brought to you by:

Reforge http://reforge.com/aakash

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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.

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Where to Find Xiankun Wu

* LinkedIn

* Threads

* Company

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Related Content

Podcasts:

* We Built an AI Employee in 62 mins

* Conversation with the CEO and Founder of Bolt

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

Newsletters:

* Context Engineering Guide

* Prompt Engineering in 2025

* How to become an AI Product Manager

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PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!

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If you want to advertise, email productgrowthppp at gmail.

Today’s Episode

The 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.

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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

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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.

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Where to Find Hamza Farooq

* LinkedIn

* Newsletter

Related Content

Podcasts:

* 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 Management

Newsletters:

* How to Become an AI Product Manager with No Experience

* How to Write a Killer AI Product Manager Resume

* How to become an AI Product Manager

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PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!

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If you want to advertise, email productgrowthppp at gmail.

Today’s Episode

Every 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.

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Check out the conversation on Apple, Spotify and YouTube.

Brought to you by:

Vanta: Leading AI compliance platform

Testkube: Leading test orchestration platform

Kameleoon: Leading AI experimentation platform

Jira Product Discovery: Plan with purpose, ship with confidence

The AI PM Certificate: Get $550 off with ‘AAKASH550C7’

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Key Takeaways

1. 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.

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Where to Find Alex Danilowicz

* LinkedIn

* Twitter/X

* Website

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Related Content

Podcasts:

* Cursor Tutorial

* Windsurf Tutorial

* AI Prototyping Tutorial

Newsletters:

* AI Agents: The Ultimate Guide for PMs

* Ultimate Guide to AI Prototyping Tools

* How to Land a $300K+ AI Product Manager Job

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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.

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If you want to advertise, email productgrowthppp at gmail.

Today’s Episode

Why 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)

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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’

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Key Takeaways

01 | Understand the 3 Types of Delight

Low 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 Rule

Allocate 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 Segmentation

Stop 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 Grid

Create 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 Technique

Ask: "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 Checklist

Before 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 Examples

Gmail 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 Obsessively

Apple'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 Win

ChatGPT 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 Later

Don'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.

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Where to Find Nesrine Changuel

* LinkedIn

* Twitter/X

* Product Delight Book

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Related Content

Podcasts:

* 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 PM

Newsletters:

* 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

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Today’s Episode

Alex 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.

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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.

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Key Takeaways

1. 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.

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Where to Find Alex Rechevskiy

* LinkedIn

* Twitter/ X

* Website

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Related Content

Podcasts:

* 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 Management

Newsletters:

* How to Become an AI Product Manager with No Experience

* How to Write a Killer AI Product Manager Resume

* How to become an AI Product Manager

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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.

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If you want to advertise, email productgrowthppp at gmail.

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.

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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

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Key Takeaways

1. 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.

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Related Content

Podcasts:

* 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 Management

Newsletters:

* How to Become an AI Product Manager with No Experience

* How to Write a Killer AI Product Manager Resume

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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.

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