Foundations
Start here — what startups are and how they work
Understand the startup world from the ground up: what startups are, how they differ from regular businesses, the stages of growth, and the mental models every founder needs before writing a single line of code.
An AI wrapper is a product built on top of a foundation model API with a custom UI, workflow, or niche focus, rather than novel AI model development.
Vertical AI is an AI product built for a specific industry or workflow, combining foundation model capabilities with deep domain expertise and proprietary data.
A one-page visual template that maps how a company creates, delivers, and captures value across nine building blocks.
B2B and B2C startups play completely different games. Here's how to choose the model that fits your market, your skills, and your capital plan.
50% of founders experience mental health conditions. Here's why founder burnout is structural - and the specific tools that actually help.
Four legal decisions - incorporation, co-founder equity, IP assignment, and the 83(b) election - can make or break your company. Get them right early.
A startup and a small business are fundamentally different organizations with different goals, capital, and exit logic. Here's the real difference.
Startups are designed to search for a repeatable, scalable business model under extreme uncertainty - they are not just small versions of large companies.
The five stages of startup growth explained - from ideation to scale - with key milestones, exit criteria, and common failure modes for each phase.
Before scaling, only one thing matters: product-market fit. Here's why it's the central challenge of early-stage startups and what it actually takes to find it.
A competitive moat is a durable advantage that protects a startup's market position from competitors. Network effects and switching costs are the strongest.
A practical guide to building AI-native companies: from defining your AI edge to raising capital and scaling your model stack.
How to validate an AI startup idea before building: test AI necessity, find the pain, prototype fast, measure willingness to pay, and plan data acquisition.
The competitive advantages that make an AI startup defensible - and why model access alone is never one of them.
A startup built from the ground up with AI as the core product architecture - not a traditional product with AI features added on top.
How product-market fit signals differ for AI products - and why the awe of early demos often masks the absence of real retention.
The flywheel effect describes how consistent momentum across linked business activities creates compounding growth with no single breakthrough moment.
Porter's Five Forces is a framework for analyzing competitive intensity in any industry across five structural forces that shape profitability.
A two-sided marketplace connects two distinct user groups who each provide value to the other, powered by cross-side network effects.
Should you build AI for businesses or consumers? An honest comparison of the dynamics, defensibility, and economics of B2B vs B2C AI.
The key AI regulations founders need to know in 2025-2026 - EU AI Act, US rules, GDPR implications, and a practical compliance checklist.
How to build sustainable competitive advantages in AI - the four real moats and how to develop them from day one.
The key metrics founders should track for AI products - from AI-specific signals to standard SaaS metrics adapted for AI economics.
Comparing the three leading AI coding tools for startup developers - paradigm, pricing, strengths, and which to choose for your team.
A clear-eyed breakdown of AI startup costs - infrastructure, inference, people, and what unit economics actually look like at different revenue stages.
Most AI wrapper startups fail within 18 months. Here's the structural reason - and the few ways to build defensibility on top of a foundation model.