Aakash Gupta

AI PM is the Job Opportunity of the Decade (Crash Course)

Hamza Farooq, who teaches AI PM at Stanford, UCLA, and Maven (and works with Home Depot, Trip Adviser, Jack in the Box), reveals the complete 6-month roadmap to go from no AI experience to PM at OpenAI or Anthropic. We built a working AI prototype live in 30 minutes (Lovable + n8n + RAG), and Hamza breaks down the 3 technical skills every AI PM must master to land a $300K+ job. Full Writeup: https://www.news.aakashg.com/p/hamza-farooq-podcast ---- Timestamps: 0:00 - Intro 1:21 - Is AI Product Management Real or Just Hype? 4:04 - Can You Become an AIPM Without Experience? 4:43 - The 6-Month Roadmap to Become an AIPM 9:13 - Live Demo: Building AI-Powered Airbnb Search 10:05 - Ads 11:24 - Building from Scratch with Webhooks & N8N 20:32 - Connecting Lovable Frontend to N8N Backend 28:16 - What is RAG and Why It Matters 36:10 - Ads 38:48 - Context Engineering vs Prompt Engineering 43:28 - Complete Roadmap: Zero to AIPM at Top Companies 46:08 - Inside Hamza's Business: Traversal AI & Teaching 51:14 - Outro ---- Thanks to our sponsors: 1. Maven: Get $ off Hamza’s course with my code AAKASHxMAVEN - https://maven.com/boring-bot/ml-system-design?utm_campaign=aakash-gupta&utm_medium=affiliate&utm_source=maven&promoCode=AAKASHxMAVEN 2. Amplitude: The market-leader in product analytics - https://amplitude.com/session-replay?utm_campaign=session-replay-launch-2025&utm_source=linkedin&utm_medium=organic-social&utm_content=productgrowthpodcast 3. Vanta: Leading AI compliance platform - http://vanta.com/aakash 4. NayaOne: Airgapped cloud-agnostic sandbox - https://nayaone.com/aakash/ 5. Kameleoon: Leading AI experimentation platform - http://www.kameleoon.com/ ---- Key takeaways: 1. AI PM salaries are skyrocketing - The median total comp for AI PMs is rapidly increasing. But now you need technical depth. Previously, you didn't need to know what RAG is or how fine-tuning works. Now you have to be a jack of all trades. 2. We built a working prototype in 30 minutes - Live demo: Lovable for front-end + n8n for workflow automation + RAG connected and working. What used to take days now takes minutes. This is the power of modern AI PM tools. 3. Context engineering is more important than prompt engineering - Prompt engineering is what you tell an LLM. Context engineering is how you design the instructions. You combine: system prompt, user prompt, memory (long-term), and RAG. This enables true personalization. 4. Know the difference: fine-tuning vs RAG - Fine-tuning = adding new vocabulary (new words). RAG = adding new knowledge (new information). Use RAG for knowledge that changes frequently. Use fine-tuning for vocabulary or specialized response patterns. 5. The 5-step architecture you need to master - Step 1: Understand what LLMs are. Step 2: Learn how to build applications. Step 3: Master prompt engineering. Step 4: Implement RAG systems. Step 5: Build agentic systems. Follow this roadmap on repeat. 6. Use the three-wave approach for building - Wave 1: Save time (efficiency gains). Wave 2: Better quality (better output). Wave 3: Completely new (novel capabilities). Start with time-savers, progress to quality improvements, end with breakthrough innovations. 7. Ask yourself 3 questions before building anything - Does it solve a user problem? Does it solve an organizational problem? Does it align with your business model? If yes to all three, build it. This validates every project. 8. Build-first mentality wins - Don't just follow roadmaps. Keep building things. You have to learn by doing. The best way to become an AI PM is to build 10+ projects and see where your products fit in solving real business problems. 9. Real-world example: Traversal.ai - Hamza's company works with manufacturers (Amazon suppliers, Jack in the Box, Home Depot). They built an army of agents processing 20,000 SKUs daily with demand forecasts. Results: better inventory optimization, planning, and cost savings. 10. Teaching accelerates your own growth - Hamza makes 10-15% of revenue from Maven courses. Why keep teaching? "I teach because I grow." His foundation course builds empathy with users. His developer course uplifts his technical skills by working on real problems with senior engineers. ---- Where to find Hamza Farooq: LinkedIn: https://www.linkedin.com/in/hamzafarooq/ Newsletter: https://boringbot.substack.com/ Where to find Aakash: Twitter: https://www.x.com/aakashg0 LinkedIn: https://www.linkedin.com/in/aagupta/ Newsletter: https://www.news.aakashg.com #aipm #productmanagement #aiproductmanager ---- About Product Growth: The world's largest podcast focused solely on product + growth, with over 195K+ listeners. Subscribe and turn on notifications to get more videos like this.

HFHamza FarooqguestAGAakash Guptahost

Nov 19, 2025·52m·Watch on YouTube ↗

📖 CHAPTERS

1. 1

   AI PM demand is real: roles, hiring signals, and comp trends

   Aakash and Hamza open by arguing that while AI has hype, AI Product Management (AIPM) roles are a real, fast-growing need because companies must turn models into usable products. Hamza points to market demand signals and rapidly rising compensation—approaching top-tier software engineering levels in some regions.

2. 2

   Why AIPMs are paid more: the new “jack-of-all-trades” PM

   Hamza explains that AIPM is not a traditional PM job anymore; it blends product judgment with technical fluency. PMs are increasingly expected to understand concepts like RAG, fine-tuning, and how systems are assembled end-to-end.

3. 3

   Can you become an AI PM without prior AI experience? The 6-month mindset

   Aakash asks whether newcomers can break in; Hamza argues yes, because the modern LLM wave is new for everyone and learnable with structure. The emphasis is on avoiding FOMO and following a concrete learning roadmap through building.

4. 4

   The simplest AI product architecture: LLM API + no-code backend + frontend

   Hamza lays out an approachable reference architecture for beginners. The stack: an LLM accessed via API, a no-code orchestration/backend layer (n8n), and a no-code frontend builder (Lovable) to ship a user-facing interface quickly.

5. 5

   Live demo: AI-powered “Airbnb natural language search” concept

   Hamza demos an unofficial Airbnb-like experience where users describe what they want in natural language and receive relevant listings via email. The demo emphasizes grounded links (not hallucinations) and highlights the PM framing: find a user pain point and prototype a solution fast.

6. 6

   From demo to build: starting the backend in n8n (plus sponsor segment)

   The conversation transitions from the end-product demo to implementation. After a sponsor break, Hamza frames the approach: don’t start with the hardest build—begin with foundational workflow concepts in n8n.

7. 7

   n8n fundamentals: triggers, agent nodes, memory, and choosing an LLM

   Hamza constructs a minimal agent workflow in n8n using a chat trigger, an LLM via OpenRouter, and memory to persist context. They discuss practical model-selection heuristics and why OpenRouter simplifies experimentation across providers.

8. 8

   Webhooks + mock data: connecting n8n to the outside world

   Hamza replaces the internal chat trigger with webhooks to enable an external frontend (or any client) to send requests and receive responses. They pin/unpin mock payloads, debug message paths (e.g., body.message), and show the core integration pattern.

9. 9

   Connecting Lovable frontend to n8n backend: end-to-end chatbot in minutes

   Hamza prompts Lovable to generate a simple chatbot UI wired to the n8n webhook endpoint. They confirm the round-trip request/response, discuss securing deployments with authentication options, and clean up outputs via frontend parsing/formatting.

10. 10

    RAG explained: enterprise unstructured data, grounded answers, and why it’s huge

    Hamza explains Retrieval-Augmented Generation as the mechanism for searching and summarizing organizational knowledge (PDFs, decks, memos). The key value is grounded, source-linked answers that scale beyond manual document search.

11. 11

    RAG in practice: rapid setup via external API + n8n HTTP node (plus sponsor block)

    After a sponsor segment, Hamza demonstrates a pragmatic shortcut: use an external RAG service (Traversal Pro) to ingest documents, then call it from n8n via an HTTP request imported from cURL—no custom coding required. They confirm via execution logs that the agent is pulling document-backed context.

12. 12

    Context engineering vs prompt engineering, plus fine-tuning basics for PMs

    Hamza reframes modern “prompting” as context engineering: combining system instructions, user input, long-term memory, and retrieved knowledge to produce personalized, accurate outputs. He contrasts this with fine-tuning, which adapts a model to consistently perform a task or adopt domain vocabulary.

13. 13

    Complete roadmap: build-your-way to AIPM using a 3-wave project strategy

    Hamza emphasizes learning by shipping repeated prototypes and aligning them to real business/user problems. He proposes three “waves” of project ideas—efficiency, quality, and net-new capabilities—to guide what learners should build as they progress toward top AIPM roles.

14. 14

    Inside Hamza’s business: Traversal AI, customer use cases, and why he teaches

    Hamza describes his startup (Traversal/Traversal.ai) and its positioning—agent-driven intelligence over operational data—sharing manufacturing and demand forecasting examples. He then explains how teaching (Maven, Stanford, UCLA, writing) supports both income and personal growth by exposing him to diverse real-world problems and builders.

15. 15

    Wrap-up: resources, courses, and where to find links

    Aakash closes by pointing viewers to Hamza’s courses, the full podcast, and a newsletter post with tools, documents, and public links referenced in the episode. The final call-to-action is to subscribe/follow and leave reviews to support future content.