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Orientation and Phase-Based Overview

The AI-Native Startup Playbook: End-to-End Roadmap

Phase 0: The Problem Phase

Phase 1: Launch Weekend

Phase 2 and Phase 3: From Believers to Product-Market Fit

Phase 2: Find Your First Believers

Phase 3: Iterate to Product-Market Fit

Phase 4: Scale and Moat

Phase 4: Pour Fuel on the Fire

Quizzes: Check Your Understanding

Quiz: Phase 0 Problem Discovery

Quiz: Launch Weekend Execution

Quiz: First Believers and Early GTM

Quiz: Iterating to Product-Market Fit

Quiz: Scaling and Building a Moat

Final Reflection and Feedback

Capstone Alignment: Your Phase Plan

Course Feedback Survey

$ (USD)

AI-Native Startup Playbook: From Discovery to Scalable Growth is a hands-on, end-to-end course for founders, product leaders, and operators who want to build durable AI-native companies. You’ll move from Phase 0 problem discovery through Phase 4 scale using a clear set of gates, checklists, and evidence standards. Rather than generic startup advice, this playbook focuses on AI leverage: data flywheels, model selection, evaluation, safety, and workflow design that compound into real moats. Across six sections, you’ll learn to identify high-urgency problems with genuine AI advantage, ship a thin end-to-end workflow in days, recruit and serve your first believers, iterate to product-market fit using rigorous evaluation and reliability engineering, and scale responsibly with security, compliance, and strong unit economics. Expect pragmatic templates, instrumentation guidance from day one, and repeatable patterns for copilots, agents, and autonomous workflows. By the end, you’ll have an evidence-backed phase plan, a de-risking experiment for your riskiest assumption, and the measurement framework to know when to advance. If you’re ready to build an AI-native product that users love—and that can scale with trust—this course gives you the roadmap and the tools.

AI-Native Startup Playbook: From Discovery to Scalable Growth

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            <p>AI-Native Startup Playbook: From Discovery to Scalable Growth is a phase-based, evidence-driven course designed to help you conceive, validate, and grow an AI-native venture from Phase 0 to Phase 4. You will learn what truly makes a startup AI-native—how to engineer data flywheels, turn model capability into durable product advantage, automate workflows responsibly, and build defensible moats. Along the way, you’ll adopt clear phase gates and checklists to determine when to move from identifying a painful problem to launching a thin-slice product, recruiting believers, achieving product-market fit, and finally scaling with confidence. The course also covers essential team roles, ethics and responsible AI by design, and the full tooling stack—data, models, evaluation, experimentation, and analytics—so you can instrument learning from day one.</p>

<p>In Phase 0, you will rigorously discover and define a high-urgency, high-frequency problem where AI confers a true advantage. You’ll practice customer discovery through jobs-to-be-done, problem interviews, and shadowing; explore solution patterns like copilots, agents, autonomous workflows, and decision support; and assess feasibility across data access, compliance, latency, accuracy, and safety. Phase 1 focuses on a launch weekend that scopes the narrowest end-to-end workflow, rapidly prototypes with prompt chains and thin UIs, establishes a data plan, and sets up instrumentation and human-in-the-loop evaluation—culminating in a go/no-go demo with willingness-to-pay signals. Phase 2 and Phase 3 guide you from recruiting 10–20 believers and running a design partner program to iterating toward PMF with a scorecard, building a continuous evaluation loop, engineering reliability and guardrails, and getting unit economics under control. Phase 4 equips you to scale: segmenting markets, picking channels and partnerships, strengthening your data/model moat, aligning org design and GTM, meeting security and compliance standards, and managing capital with metrics like LTV/CAC and burn multiple. Quizzes reinforce each phase, and a capstone-style reflection helps you craft a one-page phase plan with evidence-based exit criteria, a prioritized de-risking experiment, and a core metric with leading indicators—so you finish with a concrete roadmap to build, validate, and grow an AI-native startup.</p>
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