How to Research Any Industry From Scratch Using AI (Without Getting Fooled by Hallucinations)

How to Research Any Industry From Scratch Using AI (Without Getting Fooled by Hallucinations) The script argues that relying on ChatGPT-like tools for industry research can make users lazy because confident-sounding answers may be fabricated, and proposes NotebookLM as a better primary tool since it answers only from uploaded sources with citations. It introduces a structured hierarchy of research inputs: tier-one consultancy reports (e.g., McKinsey/BCG-style reports), tier-two government and regulatory body reports (including commissioned agency work and global bodies), tier-three company annual reports for operational and regulatory disclosures, and tier-four AI deep research used only to fill gaps. It outlines three pillars for understanding any industry, origin (history), trajectory (drivers and constraints), and engine (business model and cash flows)—built via Gemini deep research prompts and then imported into NotebookLM. It also covers organizing sources with prefixes, collecting annual reports across upstream/midstream/downstream value chains, using earnings call transcripts as near-primary research, and previews combining NotebookLM with Claude Code for more powerful execution in a future video. ⚠️ IMPORTANT: This system is intentionally constrained. In the next video, I’ll show how to connect this with Claude Code, an unrestricted AI layer that can automate and scale this entire workflow. Think of this video as the foundation. The next one is where it becomes 10x more powerful. --- 🔗 ALL RESOURCES (Shikshan Nivesh Hub): → Access the full system (prompts, templates, notebooks, masterclass): https://shikshannivesh.com/research-i... Includes: • Complete Notion workspace with all prompts • NotebookLM setups (investment + product research) • Source library (McKinsey, BCG, Deloitte, government, filings) • Full masterclass access --- 📌 WHAT YOU’LL LEARN: • Why most AI research fails (and how hallucinations mislead you) • How to build a structured AI research system from scratch • The source hierarchy: where to find reliable data for any industry • How to use NotebookLM for citation-backed research • A 3-level questioning framework: facts → synthesis → implications • How to generate deep research using AI (30,000+ words structured output) • How to extend this system into product research and decision-making --- ⏱️ TIMESTAMPS:(coming soon) 00:00 AI Makes You Lazy 00:28 Why Chatbots Mislead 02:15 NotebookLM Over ChatGPT 04:22 Industry Research Problem 05:34 Tool vs Fuel Mindset 06:11 Tier One Consulting Reports 08:28 Tier Two Government Data 10:24 Tier Three Annual Reports 11:23 Tier Four Deep Research 12:41 Three Pillars Framework 15:08 Run Gemini Research Prompts 15:27 Avoid Robotic Learning 19:04 Build NotebookLM Sources 22:18 Organize With Prefixes 24:34 Value Chain Annual Reports 29:53 Add Concall Primary Insight 33:04 Claude Code Integration 35:11 Final Thoughts and Quote --- 🧠 WHO THIS IS FOR: • Equity investors and analysts • Founders and product builders • Students learning industry research • Anyone who wants to think more clearly using AI --- 📌 TOOLS USED: • NotebookLM • Gemini Deep Research • Google Search • Comet Browser by Perplexity --- 📩 NEWSLETTER: Alpha with AI Weekly systems on AI for investing and research → https://ai.shikshannivesh.com 🌐 WEBSITE: → https://shikshannivesh.com --- 👤 ABOUT ME: I’m Shubham Borkar. I build AI-powered research systems for investors and professionals. Through Shikshan Nivesh and Alpha with AI, I focus on one thing: Helping individuals think, research, and make decisions like institutions. --- #airesearch #industryresearch #investmentresearch #notebooklm #aiforfinance