The Growth Podcast

View Top 200 Chart

Back to Rankings Page

Technology
**The Growth Podcast**

Join 500K+ for deep dives on AI + product management. After spending a decade plus in product, I now interview PM’s most insightful experts.

www.news.aakashg.com

Latest Episodes:

AI Agents for PMs in 69 Minutes — Masterclass with IBM VP Sep 05, 2025

Show notes

Today's Episode

What makes AI agents different from chatbots?

That’s the question we break down from every angle with today’s guest.

Armand Ruiz, VP of AI Platform at IBM, who has been in AI for 16 years and has become one of the most-followed AI voices on LinkedIn.

Armand leads AI platforms at IBM, building the building blocks for enterprises to build AI agents securely. He spends his time meeting with CIOs from the biggest brands who all have AI as their number one priority - and agents as one of their core components.

In our conversation, he breaks down:

* How AI agents differ from the chatbots we know

* The four-step framework every agent needs

* Why RAG systems power 90% of enterprise AI

* How product management changes when agents do the work

----

Check out the conversation on Apple, Spotify and YouTube.

Brought to you by:

* Kameleoon: AI experimentation.

* The AI Evals Course for PMs & Engineers: You get $800 with this link

* Vanta: Automate compliance, manage risk, and prove trust

* Amplitude: Try their 2-min assessment of your company’s digital maturity

* Product Faculty: Product Strategy Certificate for Leaders (Get $550 off)

----

Timestamps

00:00 Intro

02:39 What Makes AI Agents Special

04:40 The Four Steps of AI Agents

07:14 AI Agent Development Frameworks

12:59 RAG Explained

16:55 ADS

18:46 Common RAG Mistakes

26:48 Managing Multiple AI Agents

31:39 ADS

33:57 How AI Changes Product Management

37:43 Problem Investigation vs Feature Factory

41:22 Roadmap to Build AI Agents

43:30 Can Open Source AI Win?

51:39 IBM's AI Strategy

59:32 Career Journey: Intern to VP

1:02:36 Building 200K LinkedIn Followers

1:08:18 Outro----

Key Takeaways

1. AI Agents vs Chatbots: Chatbots respond to queries while agents execute complete workflows. The difference between getting suggestions and getting finished work.

2. Four-Step Agent Framework: Every agent needs Thinking (reasoning), Planning (task breakdown), Action (system execution), and Reflection (learning from outcomes).

3. RAG Dominates Enterprise: 90% of enterprise AI uses RAG to connect LLMs to proprietary data. Success requires 95%+ accuracy through sophisticated evaluation.

4. Vision RAG Unlocks Value: Most business data lives in charts and tables that traditional text-only RAG completely misses.

5. Framework Selection Matters: Use coding frameworks (LangGraph, CrewAI) for complex systems. Use no-code tools (Lindy, n8n) for rapid prototyping.

6. PM Ratios Transform: Traditional 1:6-10 PM-to-developer ratios become 1:2-30 when agents handle research and documentation.

7. Prototypes Beat PRDs: Show working systems instead of 20-page documents teams misinterpret. AI enables functional demos.

8. Open Source Wins: Despite closed-source capabilities, enterprises choose open source for licensing control and infrastructure flexibility.

9. Technical Literacy Essential: Understanding agents, RAG, and frameworks becomes baseline knowledge for everyone, not just developers.

10. Implementation Reality: Enterprise RAG needs heavy data engineering. Teams underestimate accuracy requirements and engineering complexity.

----

Related Content

Podcasts:

We Built an AI Agent to Automate PM in 73 mins

We Built an AI Employee in 62 mins

5 AI Agents Every PM Should Build

Newsletters:

AI Agents: The Ultimate Guide for PMs

AI Evals for Agents

Step-by-Step RAG

----

P.S. More than 85% of you aren't subscribed yet. If you can subscribe on YouTube, follow on Apple & Spotify, my commitment to you is that we'll continue making this content better.

----

If you want to advertise, email productgrowthppp at gmail.

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribe

AI Product Leadership Masterclass: The Makings of a Manager (With Author of the Book) Sep 01, 2025

Show notes

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 you

This 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 inspiration

Jira Product Discovery: Build the right thing, reliably

Product Faculty : Product Strategic Certificate for Leaders (Get $550 off)

The AI Evals Course for PMs & Engineers: You get $800 with this link

----

Timestamps

00:00 Intro

02:30 The Death of Product Development

08:42 Learn The Craft

15:02 ADS

17:00 Definition of a Managers's Job

21:12 Julie's Thoughts on AI Agents

28:12 Blindspots While switching from IC to Manger

30:40 ADS

35:48 The Three Levers That Never Change

41:20 What is Feedback

46:43 How AI is Changing the Domain

52:49 What Makes Great AI Product Leaders Different

1:00:55 Essential AI Tools Every Leader Should Master

1:09:15 Lessons from OpenAI's Product Team

1:15:55 Outro

----

Key Takeaways

1. 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 Content

Podcasts:

Full Roadmap: Become an AI PM

Complete Course: AI Product Management

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

Newsletters:

How to become an AI Product Manager

How to Write a Killer AI Product Manager Resume

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

Complete Course: AI Experimentation Aug 29, 2025

Show notes

"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 experimentation

How to experiment with AI features

Last 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 Overview

01:54 The 4 Steps of Experimentation Framework

14:12 ADS

16:00 How AI has Changed Experimentation

21:08 User Behaviour Models

26:56 Multi-Armed Bandit vs Contextual Bandit

30:05 ADS

31:55 AI Content Genration

35:13 How Vibe Coding Changes Experimentation

41:35 Live Demo From Idea to Running Experiment in 2 Minutes

43:36 Two-Minute Build Achievement

51:49 How to Measure AI Features Properly

54:17 Measuring RAG Systems 3 Key Metrics

01:07:18 Best Experimentation Company Booking.com

01:10:10 Biggest PM Mistakes in Experimentation

01:13:52 Ending

----

Key Takeaways

1. 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

OpenAI Head of Product (Integrity) on the Future of AI Agents, PM, and AI threats Aug 24, 2025

Show notes

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

Full show notes at the publisher

He built the top AI agent startup | Flo Crivello, Former PM, now CEO & Founder, Lindy AI Aug 18, 2025

Show notes

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 inspiration

Jira Product Discovery: Build the right thing

Product Faculty: #1 AI PM Certification (Class Starts: 15 Sep, get $500 off)

----

Timestamps:

AI Agents Can Replace Your Team - 00:00:00

The Top 5 AI Agents Every Entrepreneur Needs - 00:03:13

The Golden Framework: When to Build an Agent - 00:08:50

Keeping AI Agents Safe Without Killing Innovation - 00:09:42

From Doer to Orchestrator: The New Management Mindset - 00:11:08

Lindy vs ChatGPT: Individual Tools vs Work Platforms - 00:13:23

Managing Agent Performance, Permissions & Costs - 00:14:26

AD: Mobbin - 00:15:56

AD: Jira Product Discovery - 00:16:55

Managing Context and Token Costs - 00:17:51

Lindy vs Competitors and Zapier - 00:18:58

Inside Lindy's 5x Growth in 6 Months - 00:20:38

The Salesperson Managing 40 AI Employees - 00:21:45

The Soham Scandal: Hiring the 5-Job Engineer - 00:23:20

From PM to AI Visionary: The Founding Story - 00:28:24

AD: Maven - 00:31:21

AD: AI PM Certification - 00:32:08

The Pivot Philosophy: Action Produces Information - 00:32:54

Should Every PM Become an AI Founder? - 00:37:17

Talk to Customers, Build Product: Skip Everything Else - 00:38:45

The Valley of Death: Surviving the Hardest Pivot - 00:41:02

The AI Agent Agency Gold Rush - 00:44:08

Big Tech is Missing the AI Agent Revolution - 00:47:42

The 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 CEO

This $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

Teresa Torres' Step-by-Step Guide to AI Product Discovery Aug 12, 2025

Show notes

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 tools

2. How to discovery for AI features

If 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 workspace

Jira Product Discovery: Build the right thing

Parlance Labs: Practical consulting that improves your AI

Product Faculty: #1 AI PM Certification (Class Starts: 15 Sep, get $500 off)

----

Timestamps:

Teresa's Background - 0:00

Story-Based Interviewing - 3:20

Fake Discovery Signs - 4:08

Assumption Testing - 4:39

Continuous Discovery Framework - 5:35

AI Changes Discovery - 8:01

AI Synthesis Concerns - 9:21

AI Prototyping Era - 12:45

Ads - 15:45

AI Prototyping Workflow - 17:32

Common Interview Mistakes - 22:24

Interview Synthesis - 24:26

OST Updates - 28:53

Discovery Theater - 30:52

Ads - 32:15

Real Product Management - 34:03

AI Product Discovery - 35:29

Context Engineering - 39:16

Orchestration Explained - 42:03

Error Analysis - 46:01

Observability & Traces - 46:05

Claude Code Demo - 49:15

Business 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 Course

How to Do Product Discovery Right with Pawel Huryn

Marty Cagan on the 4 Key Risks and Importance of Discovery

How 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

Building in Public: The 7 AI Tools I'm Using in My $1M+/Yr Business Aug 09, 2025

Show notes

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 canvas

Jira Product Discovery: Build the right thing

Mobbin: Discover real-world design inspiration

Parlance Labs: Practical consulting that improves your AI

----

Timestamps:

Preview – 00:00:00

AI Workflow 1 (Zapier) – 00:00:48

AI Workflow 2 (v0) – 00:03:13

AI Workflow 3 (Cursor) – 00:08:55

AI Workflow 4 (v0.3) – 00:12:04

AI Workflow 5 (Lindy) – 00:16:32

AI Workflow 6 (Riverside) – 00:19:03

AI Workflow 7 (Claude Copilot) – 00:22:47

Summarizing Everything – 00:27:52

----

Check out the conversation on Apple, Spotify and YouTube.

----

Related Podcasts:

This PM Built a Six-Figure ($100K+) AI Side Hustle

He Built a $2M/Yr One-Person Business - Steal His Playbook

This PM was Laid Off - Now he has 125K followers

Her Layoff Went Viral - Now She has 300K+ Subscribers

How 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

Full Roadmap: Become an AI PM in 2025 | Ankit Shukla, HelloPM Founder Jul 27, 2025

Show notes

If this 80 minutes podcast doesn't make you an AI PM, I'll delete my channel!

Yes, I know what I just said. But this is too good!

You’ll learn:

- How to become an AI PM without any tech experience

- 2 main categories of AI PMs and how to break into each

- How to build great AI products your users love

- All concepts like Evals, RAG, MCP, etc

- Creating AI PM portfolio

And so much more

----

Brought to you by:

The AI Evals Course for PMs & Engineers: You get $800 with this link

Maven: Get $100 off my curation of their top courses

AI PM Certification: Get $500 with code AAKASH25

----

Timestamps:

Two Types of PMs in the World Right Now – 00:00:00

How Much AI PMs Make in the US & India – 00:01:49

Why Jobs Aren’t Marketed as “AI PM” Roles – 00:04:19

Live Slide Share Session Begins (AI PDLC) – 00:05:35

What People Get Wrong About AI PM Jobs – 00:08:51

Why AI Product Management Is Here to Stay – 00:11:00

Product Development Lifecycle Explained – 00:12:35

Ad 1: AI Evaluations – 00:14:47

Ad 2: Maven Courses – 00:15:46

Understanding PM Canals (Idea Sources) – 00:16:34

How AI Will Transform the Traditional Product Cycle – 00:18:55

The Two Branches of AI: Predictive vs Generative – 00:23:10

Diving Deeper into Generative AI – 00:27:17

Ad 3: AI PM Certification – 00:28:47

How to Build Your Own AI Use Case Database – 00:29:34

Why You Shouldn’t Use AI for Everything – 00:34:10

Understanding Problem Space vs Solution Space – 00:36:51

Most Common Mistake People Make Entering AI PM – 00:40:49

The Building Blocks of AI – 00:47:01

Contextualization (Prompts, Fine-Tuning, RAG) – 00:50:24

The Limitations of AI — Why You Still Need Evals – 00:56:18

Case Study: AI-First Job Search Website – 00:58:49

Prompt Engineering Breakdown for the Case Study – 01:02:09

Understanding and Leveraging AI Agents – 01:03:40

Introducing the MCP Framework – 01:08:07

How to Build Your AI PM Portfolio – 01:11:37

Your Next 7 Steps to Becoming an AI PM – 01:16:04

Closing Thoughts and Final Notes – 01:18:41

----

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.

----

Check out the conversation on Apple or Spotify and the demo on YouTube.

----

Related Podcasts:

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

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

Complete Course: AI Product Management

Tutorial of Top 5 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

How Linear Built a $1.25B Unicorn with Just 2 PMs Jul 19, 2025

Show notes

Nan Yu shares Linear's proven product-building method that helped them took Linear to a $1.25B product with just 2 PMs.

You’ll learn:

- How top companies like Linear are actually building AI features

- Secrets to building a lean product that users genuinely love

- Nan Yu’s advice for landing top PM jobs.

- The Linear method in detail

----

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

AI PM Certification: Get $500 with code AAKASH25

Maven: Get $100 off my curation of their top courses

----

Timestamps:

Preview – 00:00:00

Why are all the GIANTS using Linear? – 00:02:09

Linear's Core Functionality – 00:04:07

Ad (AI Evals) – 00:04:44

Ad (Vanta) – 00:05:44

Linear Method: How They Build Product – 00:06:36

Principles of the Linear Method – 00:09:16

Saying No to Busy Work / Busy Work Is a Result of “Lack of Clarity” – 00:12:14

Linear's Roadmap Planning Horizon – 00:14:43

Ad (AI PM) – 00:15:06

Ad (Maven) – 00:15:53

Planning Process – 00:16:41

Linear's Take on OKRs – 00:19:13

Setting The Bar High – 00:26:06

Story: Building The Smallest Version Possible – 00:28:49

Public Roadmaps are DANGEROUS – 00:34:20

Applying Linear Method to AI Features (Agents as Users) – 00:38:18

Mantra: Solve Real Problems for Real People – 00:43:08

Landing a Head of Product Role at Linear – 00:46:08

Story: How the Initial Contact with Linear Happened – 00:48:16

Advice for Aspiring PMs at Linear – 00:49:50

Making it to unicorn status with 2 PMs & 40 engineers – 00:53:18

Everlane's Pricing Strategy (Cost-Plus) – 00:53:44

Shift from B2C Retail to Product-Led B2B SaaS – 00:55:10

Can Today's PMs Shift Industries? – 00:57:22

Closing Notes – 00:59:49

----

Key Takeaways:

Takeaways:

1. Don't Chase AI Hype, Solve Real Pain: Instead of building flashy AI features (that no one needs), Linear observes where users are actually struggling (like juggling many AI tools for async work) and builds direct solutions. It's about fixing real problems, not just jumping on trends.

2. Your Users Aren't a Feature Checklist. Listen intently to user feedback, but don't take every request literally. The goal is to understand their core frustrations and goals, not just to say "yes" to every demand that could lead to bloat.

3. Purpose Over PR Stunts. Forget building the "loudest possible feature." Linear's core mantra is to solve real problems for real people. Focus on making the most useful thing, and the noise will follow.

4. Run Marathons, Not Sprints. Sustainable momentum beats endless "sprints" every DAMN time. Find a comfortable pace you can maintain not just tomorrow, but a year from now. Burnout is a warning sign, not a badge of honor.

5. Scope Small, Ship Often. Unshipped code is debt. By making tasks as tiny as possible, you get the reward of shipping constantly. This creates tangible proof of work, gets immediate user feedback, and keeps the motivation high.

6. Quality Isn't a Side Quest. Bugs aren't "minor." They're either critical or high priority. A "little bug" seen a hundred times tells customers you don't care. Quality work is baked into every feature, because a feature isn't done until it's high quality.

7. Clear Talk, Clear Work. Banjargon. LITERALLY. Words should have one meaning. If "issue" refers to a task in your product, it only refers to that. This ruthless clarity eliminates misunderstandings and speeds up collaboration.

8. Ditch Old-School User Stories. Traditional "As a user, I want X" stories often hide context and slow things down. Everyone understands software now. Cut the BS. Be direct and state the desired outcome clearly.

9. Build to Learn, Not to Delay. Excessive data collection or endless A/B tests shows lack of decision making. Instead, build a smaller version of your idea quickly, put it out there, and let the market tell you if you're right or wrong. Learn fast, move faster.

10. Forget Personas, Find Mike. Stop building for abstract personas. Identify the real people you're working with – "Mike from Brex," "Sarah from Acme Corp." Knowing them by name deepens empathy and leads to better solutions.

----

Check it out on Apple, Spotify, or YouTube.

----

Related Podcasts:

Complete Course: AI Product Discovery

"Most Product Managers are Bullsh*t Managers" -2x CPO

----

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

The PM’s Role in AI Evals: Step-by-Step Jul 11, 2025

Show notes

Today, we’ve got some of our most requested guests yet: Hamel Husain and Shreya Shankar, creators of the world’s best AI Evals cohort.

You’ll learn:

- Why AI evaluations are the most critical skill for building successful AI products

- What common mistakes people are making and how to avoid them

- How to effectively "hill climb" towards better AI performance

If you're building AI features, or aiming to master how AI Eval actually works, this episode is your step-by-step blueprint.

----

Brought to you by:

The AI Evals Course for PMs & Engineers: You get $800 with this link

Jira Product Discovery: Plan with purpose, ship with confidence

Vanta: Automate compliance, security, and trust with AI (Get $1,000 with my link)

AI PM Certification: Get $500 with code AAKASH25

----

Timestamps:

00:00:00- Preview

00:02:06 - Three reasons PMs NEED evals.

00:04:40 - Why PMs shouldn't view evals as monotonous

00:06:23 - Are evals the hardest part of AI products solved?

00:07:37 - Why can't you just rely on human "vibe checks"?

00:12:11 - Ad 1 (AI Evals Course)

00:13:10 - Ad 2 (Jira Product Discovery)

00:14:06 - Are LLMs good at 1-5ratings?

00:15:45 - The "Whack-a-mole" analogy without evals

00:16:26 - Hallucination problem in emails (Apollo story)

00:21:22 - How Airbnb used machine learning models?

00:23:56 - Evaluating RAG Systems.

00:29:52 - Ad 3 (Vanta)

00:30:56 - Ad 4 (AIPM Certification on Maven)

00:31:42 - Hill Climbing

00:35:51 - Red flag: Suspiciously high eval metrics

00:39:02 - Design principles for effective evals

00:42:42 - How OpenAI approaches evals

00:44:39 - Foundation models are trained on "average taste"

00:49:36 - Cons of fine-tuning

00:51:27 - Prompt engineering vs. RAG vs. Fine-tuning

00:53:00 - Introduction of "The Three Gulfs" framework

00:56:04 - Roadmap for learning AI evals

01:01:41 - Why error analysis is critical for LLMs

01:08:29 - Using LLM as a judge

01:10:15 - Frameworks for systematic problem-solving in labels

01:17:42 - Importance of niche and qualifying clients. (Pro tips)

01:18:43 - $800K for first course cohort!

01:20:15 - Why end a successful cohort?

01:25:49 - GOLD advice for creating a successful course

01:33:39 - Outro

----

Key Takeaways:

1. Stop Guessing. Eval Your AI. Your AI isn’t an MVP without robust evaluations. Build in judgment — or you’re just shipping hope. Without evaluation, AI performance is a happy accident.

2. Error Analysis = Your Superpower. General metrics won’t save you. You need to understand why your AI messed up. Only then can you fix it — not just wish it worked better.

3. 99% Accuracy is a LIE. Suspiciously high metrics usually mean your evaluation setup is broken. Real-world AI is never perfect. If your evals say otherwise, they’re flawed.

4. Fine-Tuning is a Trap (Mostly). Fine-tuning is expensive, brittle, and often unnecessary. Start with smarter prompts and RAG. Only fine-tune if you must.

5. Your Data’s Wild. Understand It. You can’t eyeball everything. Without structured evaluation, you’ll drown in noise and never find patterns or fixes that matter.

6. Models Fail to Generalize. Always. Your AI will break on new data. Don’t blame it. Adapt it. Use RAG, upgrade inputs, and stop expecting out-of-the-box magic.

7. OpenAI Doesn’t Get Your Vibe. Their models are average-taste. Your product isn’t. If you want your brand’s voice in your AI, you must define it yourself — with evals.

8. Trust LLM Judges... but validate them hard. LLMs can scale your evals — but you still need to verify them against human-labeled data. Don’t blindly trust your judge.

9. Your Prompts Are S**T. If your AI is bad, it’s probably your fault. The cheapest, most powerful fix? Sharpen your prompts. Clearer instructions = smarter AI.

10. Let AI Teach You. Seriously. LLM judges aren’t just scoring you — they can teach you. Reviewing how your AI fails is the best way to learn what great outputs should look like.

----

Check it out on Apple, Spotify, or YouTube.

----

Related Podcasts:

Complete Course: AI Product Management

Tutorial of Top 5 AI Prototyping Tools

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

College Dropout Raised $20M Building AI Tools | Cluely, Roy Lee

Bolt CEO and Founder on How he Hit $30M ARR in a Year

LogRocket CEO and Founder on How to Build a $100M+ AI Startup

Amplitude CEO and Founder on Building the Product Analytics Leader

----

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

Our TOPPODCAST Picks

Follow Us

Stay Connected

Related Podcasts

Links

Stay Connected