Startup Bootcamp Series

The AI Founder's Handbook

Building an AI Startup in 2026

A field guide for founders building at the frontier

Curated by Arunabh Bhattacharjee · github.com/arubhatt

Part I Foundations

01Introduction to AI Startups Why an AI startup is a different species.02AI Fundamentals I: Models, Modalities & Inference The technical bedrock, part one.03AI Fundamentals II: Prompting, RAG, Agents & Tooling The technical bedrock, part two.

Part II Market & Validation

04Markets, Users & AI Product Strategy Where AI creates real value, not gimmicks.05Data Strategy & the Data Flywheel Turning usage into a compounding advantage.06Customer Validation for AI Evals as the new customer development.

Part III The Stack

07Choosing Model Providers & Hugging Face Pick a model supplier without betting the company.08The Content & Data Stack Vector DBs, RAG infrastructure, pipelines.09Infrastructure, Observability & LLMOps Running models like a grown-up.10Production Evals & Monitoring Keeping quality from silently drifting.

Part IV Go-to-Market & Moat

11Selling AI & Customer Success Trust, ROI proof, and security reviews.12Defensibility & the AI Moat Data, workflow, and distribution.

Part V Company Building

13Finance & Unit Economics for AI Inference COGS and gross-margin reality.14Venture Funding for AI The 2026 investor landscape.15Pitfalls & Crises in AI Regressions, incidents, and provider risk.

Part VI People

16Team, Culture & Leadership for AI Research, engineering, product — one org.

Part VII Responsibility

17Responsible AI: Safety, Ethics & Regulation The EU AI Act and governance in 2026.