Part I: Foundations¶
Understanding the fundamentals of what makes a company truly AI-first.
Overview¶
Before diving into tactics, we establish why being AI-first matters, what it actually means, and the landscape of tools and providers you'll need to navigate. This section covers the strategic rationale, the mindset shift required, and the foundational technology decisions that everything else builds upon.
Chapters in This Part¶
Chapter 1: The AI-First Imperative¶
The case for starting AI-first and the competitive advantages it creates. We explore the difference between AI-first and AI-enabled companies, first-mover advantages, and the inverted economics of building with AI.
Chapter 2: The AI-First Mindset¶
How AI-first founders think differently about opportunities and building. Covers agent-first design, build vs buy calculus, data as product, and human-AI collaboration.
Chapter 3: The AI Landscape — Models, Providers, and Aggregators¶
Navigating foundation models, providers, and aggregators. A decision framework for choosing between OpenAI, Anthropic, Google, open source, and routing strategies with OpenRouter.
Key Concepts Introduced¶
- AI First vs AI Enabled - The fundamental distinction
- Build vs Buy Calculus - Strategic framework for capability decisions
- Data Flywheel - Self-reinforcing data loops
- Probabilistic AI - Managing uncertainty in AI outputs
- Human AI Collaboration - Designing effective human-AI teams
- Model Routing - Multi-provider strategies and aggregation
Reading Guide¶
For business leaders: Focus on Chapters 1-2 for strategic context, then skim Chapter 3 for key decision frameworks.
For technical leaders: All chapters are relevant, with Chapter 3 providing the deepest technical guidance on model selection and routing.
For startup founders: Read all three chapters sequentially—these are your foundational decisions.