The Growth Podcast

Today’s episode
Stop applying to AI PM jobs until you understand the fundamentals.
That is not gatekeeping. That is the MIT finding. 19 out of 20 AI pilots fail. The #1 reason? Picking the wrong problem to apply AI to.
Not the wrong model. Not the wrong data. The wrong problem.
Jyothi Nookula has spent 13.5 years in AI. 12 patents. AIPM at Amazon (SageMaker), Meta (PyTorch), Netflix (Developer Platform), and Etsy.
She has hired AIPMs at three of those companies. Trained 1,500+ PMs to transition into AI roles.
If you are trying to break into AI PM, this is the one episode to watch.
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Brought to you by
* Product Faculty: Get $550 off their #1 AI PM Certification with my link
* Amplitude: The market-leader in product analytics
* Pendo: The #1 software experience management platform
* NayaOne: Airgapped cloud-agnostic sandbox for AI validation
* Kameleoon: Prompt-based experimentation for product teams
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If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.
If you want my PM Operating System in Claude Code, click here.
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Key Takeaways:
1. Two types of AIPM roles exist - 80% are traditional PM roles with AI features added on, where the core product existed before AI. 20% are AI native roles where the product IS AI and the value proposition is impossible without it. Know which type before you apply.
2. The AI PM stack has three layers - Application PMs own user experience (60% of roles, easiest entry point). Platform PMs build tools for other builders (30%). Infra PMs build foundational systems like vector databases and GPU orchestration (10%).
3. 19 out of 20 AI pilots fail from wrong problem selection - AI makes sense for complex pattern recognition, prediction from historical data, and personalization at scale. If explainability is non-negotiable, rules exist, data is limited, or speed is critical, start with heuristics.
4. Most teams overcomplicate their AI technique choice - If you can put the problem in a spreadsheet with inputs and an output to predict, traditional ML is the answer. Perception problems need deep learning. Natural language reasoning needs Gen AI. These are not competitors, they are tools in your toolkit.
5. AI products are fundamentally probabilistic - The same input can produce different outputs. AIPMs must think in quality distributions and acceptable error rates, not binary success vs failure. Data is a first-class citizen, not a nice-to-have.
6. Agents decide, workflows follow steps - Workflows have predetermined sequences with deterministic outcomes. Agents receive goals and independently decide which tools to use. The live N8N demo showed identical tools producing completely different execution patterns.
7. Context engineering is the real production skill - Claude Sonnet has a 200K token context window but that fills fast with knowledge bases, conversation history, and real-time data. Every token costs money. Managing what to load and when directly impacts both quality and cost.
8. Follow the hierarchy before fine tuning - Prompt optimisation first, then context engineering, then RAG. 80% of use cases get solved with RAG. Fine tuning should only be considered after exhausting all three.
9. Build products not projects - Launch your AI work, get real users, encounter real breakage. That gives you richer interview material than any course certificate. Build an agent, build a RAG system, and build an app that solves a real problem.
10. PM culture at big tech shapes who you become - Amazon PMs spend 40-50% of time writing PRFAQs and six-pagers. Meta PMs live in experimentation and statistical significance. Netflix PMs operate with full autonomy through context over control. Each teaches something different.
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Where to find Jyothi Nookula
* LinkedIn
* NextGen Product Manager
Related content
Podcasts:
* Naman Pandey on OpenClaw
* Lisa Huang on Gemini Gems
* Frank Lee on Amplitude and MCP
Newsletters:
* The ultimate guide to context engineering
* RAG vs fine tuning vs prompt engineering
* AI foundations for PMs
PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!


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

Today’s episode
Most PMs treat evals like a quality gate. Something you run right before shipping, just to check the box.
That is backwards.
The best AI product teams treat evals as the starting point. They write the eval before the prompt. They iterate on the scoring function before the model. They use failing evals as a roadmap.
That shift is what today’s episode is about.
I sat down with Ankur Goyal, Founder and CEO of Braintrust. It is the eval platform used by Replit, Vercel, Airtable, Ramp, Zapier, and Notion. Braintrust just announced its Series B at an $800 million valuation.
Users are running 10x more evals than this time last year. People log more data per day now than they did in the entire first year the product existed.
In this episode, we build an eval entirely from scratch. Live. No pre-written prompts, no pre-written data. We connect to Linear’s MCP server, generate test data, write a scoring function, and iterate until the score goes from 0 to 0.75.
Plus, we cover the complete eval playbook for PMs:
If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.
If you want my PM Operating System in Claude Code, click here.
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Check out the conversation on Apple, Spotify, and YouTube.
Brought to you by:
* Kameleoon: Leading AI experimentation platform
* Testkube: Leading test orchestration platform
* Pendo: The #1 software experience management platform
* Bolt: Ship AI-powered products 10x faster
* Product Faculty: Get $550 off their #1 AI PM Certification with my link
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Key Takeaways:
1. Vibe checks are evals - When you look at an AI output and intuit whether it is good or bad, you are using your brain as a scoring function. It is evaluation. It just does not scale past one person and a handful of examples.
2. Every eval has three parts - Data (a set of inputs), Task (generates an output), and Scores (rates the output between 0 and 1). That normalization forces comparability across time.
3. Evals are the new PRD - In 2015, a PRD was an unstructured document nobody followed. In 2026, the modern PRD is an eval the whole team can run to quantify product quality.
4. Start with imperfect data - Auto-generate test questions with a model. Do not spend a month building a golden data set. Jump in and iterate from your first experiment.
5. The distance principle - The farther you are from the end user, the more critical evals become. Anthropic can vibe check Claude Code because engineers are the users. Healthcare AI teams cannot.
6. Use categorical scoring, not freeform numbers - Give the scorer three clear options (full answer, partial, no answer) instead of asking an LLM to produce an arbitrary number.
7. Evals compound, prompts do not - Models and frameworks change every few months. If you encode what your users need as evals, that investment survives every model swap.
8. Have evals that fail - If everything passes, you have blind spots. Keep failing evals as a roadmap and rerun them every time a new model drops.
9. Build the offline-to-online flywheel - Offline evals test your hypothesis. Online evals run the same scorers on production logs. The gap between them is your improvement roadmap.
10. The best teams review production logs every morning - They find novel patterns, add them to the data set, and iterate all day. That morning ritual is what separates teams that ship blind from teams that ship with confidence.
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Where to find Ankur Goyal
* LinkedIn
* Braintrust
Related content
Newsletters:
* AI evals explained simply
* AI observability for PMs
* How to build AI products
Podcasts:
* AI evals with Hamel Husain and Shreya Shankar
* AI evals part 2 with Hamel and Shreya
* Aman Khan on AI product quality
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PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!
If you want to advertise, email productgrowthppp at gmail.


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

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

Today’s episode
Most PMs start every day like this. Open the calendar. Open the CRM. Open Slack. Open the meeting notes. Open LinkedIn. Piece together what matters. Lose 30 minutes before real work even starts.
That is not how the best PMs are working anymore. The best PMs are running one command in the morning and getting everything they need in five minutes. Their calendar, their deals, their market intel, their career gaps, all pulled together automatically.
That shift is what today’s episode is about.
I sat down with Dave Killeen, Field CPO at Pendo.io. He has worked at BBC, Mail Online, and now runs the field product function at one of the largest product management platforms in the world. He has 25 years in product. Over the last few months, he built a full personal operating system called DEX in Claude Code, open sourced it on GitHub, and it is getting serious traction.
In this conversation, Dave walks through his entire system live on screen. You will see how he runs a daily plan, creates PRDs from a backlog, manages parallel workstreams on a Kanban board, and tracks his career goals, all from one terminal window. And you will learn the three building blocks that make it all work.
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Check out the conversation on Apple, Spotify and YouTube.
Brought to you by
* Pendo: The #1 software experience management platform
* Jira Product Discovery: Plan with purpose, ship with confidence
* Amplitude: The market-leader in product analytics
* NayaOne: Airgapped cloud-agnostic sandbox
* Product Faculty: Get $550 off the AI PM Certification with code AAKASH550C7
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If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.
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Key Takeaways:
1. One command replaces your morning routine - Dave's daily plan slash command pulls from calendar, CRM, Granola, LinkedIn, YouTube, and 120 newsletters in five minutes. No tab switching. No manual assembly.
2. MCP servers are the key to connecting everything - Point Claude at any API documentation with your API key and it builds an MCP server for you. MCP provides structured guardrails that make the AI's behavior consistent and deterministic.
3. Skills, MCP, and hooks are three different things - Skills are plain English job descriptions for what the AI should do. MCP servers are structured integrations for connecting external services. Hooks are triggers that fire at specific conversation moments.
4. Session start hooks make the system compound - Every new Claude Code chat gets injected with weekly priorities, quarterly goals, working preferences, and past mistakes. The AI never starts from scratch.
5. Living markdown files are the compounding mechanism - Every project, person, and company gets a markdown file that accumulates context from meetings, messages, and intel over time. The more you use the system, the smarter every file becomes.
6. You can build a mobile app in 37 minutes - Dave built the full app with Claude and spent more time in Xcode publishing it. The constraint is taste, not building speed.
7. The AI should hold you accountable - Dave's Claude MD file includes "harsh truths for Dave" that the AI wrote after auditing his system. This gets injected into every session to prevent the same mistakes.
8. Career planning should compound like product data - A career MCP server collects evidence, runs gap analysis, and calculates promotion readiness. When review time comes, the evidence is already assembled.
9. Be precise about your goal, not the path - The kindest thing you can do for the AI is give it a very clear destination. Do not tell it how to get there. Let it figure out the most elegant approach itself.
10. Voice-first changes everything - Using Whisperflow or Super Whisper instead of typing fundamentally changes how you interact with Claude. You think out loud. The conversation flows. You build faster.
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Where to find Dave Killeen
* LinkedIn
* Pendo
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Related content
Newsletters
* The PM operating system guide
* How to use Claude Code like a pro
* Master AI agent distribution
* Claude Cowork and Code setup guide
* The AI PM tool stack
Podcasts
* Frank Lee on Claude Code and MCP workflows
* Carl Vellotti on Claude Code operating systems
* Rachel Wolan on AI PM workflows
* Caitlin Sullivan on building with Claude
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PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!
If you want to advertise, email productgrowthppp at gmail.


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

Today’s episode
Most PMs are using AI the same way they used Google in 2005.
Type something in. Get something out. Move on.
That is not how the best PMs are using it. The best PMs have stopped treating AI as a search engine and started treating it as a team member. One that already knows their product, their writing style, their strategy. One that does not need to be briefed from scratch every single time.
That shift is what today’s episode is about.
I sat down with Lisa Huang, SVP of Product at Xero, an $18 billion finance platform. She built the AI assistant for the first generation Meta RayBan smart glasses. She created Gemini Gems at Google. She has been an AI PM at Apple, Meta, and Google - three of the most demanding AI product environments in the world.
She gave us a masterclass across Gemini Gems, building AI into hardware, running AI agents at scale inside a financial product, and what the AI PM career actually looks like from here.
In today’s episode, we discuss across three topics.
* How to build Gemini Gems and AI projects that actually work.
* What she learned building AI into a wearable device.
* What the future of the AI PM career actually looks like.
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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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If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.
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Key takeaways:
1. Stop briefing your LLM from scratch every time - Gemini Gems hold your context permanently. Your role, your company strategy, your writing style. Build it once and it already knows everything the next time you open it.
2. Every PM needs 3 Gems - A writing clone trained on your PRDs and emails. A product strategy advisor loaded with your company docs and competitor analysis. A user research synthesizer that ingests raw transcripts and surfaces key themes.
3. Vague instructions are the number one mistake - "Help me write better" gets you nothing. Write a full page of context. Your role, your audience, your format preferences. The more specific, the more personalized the output.
4. Gemini Gems vs ChatGPT custom GPTs - OpenAI framed GPTs as an app store ecosystem. Google focused on personal productivity instead. First principles beat copying a competitor's framing, and the GPT store never took off.
5. On-device AI is the future for wearables - Cloud is the default today but once a device is on your face all day, people want their data staying local. Privacy beats performance when the device is that personal.
6. Accuracy is the product in high-stakes AI - LLMs out of the box are not great at math, accounting, or tax. Winning agents combine deep domain knowledge with proprietary data that no general-purpose model can access.
7. Measure agents in three layers - Quality first (evals, human annotators, LLM judges). Product metrics second (adoption, retention, CSAT). Business impact third (revenue attribution, ARR). Skip to layer three without the foundation and you are measuring on sand.
8. AI will not replace PMs - it will replace the execution work. Writing PRDs, creating mocks, managing roadmaps. What stays is product judgment. The ability to look at ambiguous signals and say this is the right bet and here is why.
9. The PM role is becoming a hybrid - PM to engineer ratios will compress. The expectation is that PMs also build. Not just spec and hand off, but prototype, design, and code enough to show what they mean. The tools to do this exist right now.
10. Your company's permission is not required - Most companies are not fine-tuning models. They are using the same consumer tools you already have. Build Gems. Build projects. Build small AI products with your personal data. There is nothing stopping you.
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Where to find Lisa Huang
* LinkedIn
* Website
Related content
Newsletters
* How to become an AI PM
* Practical AI agents for PMs
* AI evals explained simply
* AI product strategy
* The AI PM learning roadmap
Podcasts
* Claude Code + Analytics - Vibe PMing with Frank Lee
* AI evals explained simply with Ankit Shukla
* How to become an AI PM with Marily Nika
* AI prototyping mastery with Sachin Rekhi
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PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!
If you want to advertise, email productgrowthppp at gmail.


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