Go-to-Market and Distribution for AI Products

An AI product reaches customers in one of six ways: through the product itself (a free tier or a viral output that sells the paid tier: product-led growth), through developers (an API or SDK that engineers adopt before procurement notices), through a sales team (enterprise selling with pilots, security reviews and proof-of-value), through a cloud or marketplace (AWS, Azure, Google Cloud and their marketplaces, where AI spend can be drawn from a customer's existing cloud commitment), through someone else's platform (app stores, operating systems, browsers, messaging apps, telecom bundles, default-placement deals), or inside an incumbent's existing product (bundling: Copilot in Microsoft 365, Gemini in Workspace and Android, Einstein and Agentforce in Salesforce). The striking fact of 2023–26 is how fast the first two routes worked: coding tools and app builders reported going from launch to hundreds of millions or billions of dollars of annualised revenue in one to three years, numbers that classic SaaS took a decade to reach. The equally striking fact is how fragile that speed can be. A product that grows on someone else's model, someone else's platform and someone else's distribution can lose all three in one negotiation, as Windsurf discovered in 2025. Distribution in AI is therefore an answer to four questions: who decides to adopt (a user, a developer, a buyer, a platform owner), what does the first useful experience cost the vendor in compute, who owns the customer relationship after adoption, and what stops an incumbent with existing distribution from shipping the same feature as a bundle? This section maps the routes to market, the economics and evidence for each, the platform and bundling dynamics that decide who wins, landmark cases, pitfalls and how practitioners think about distribution. Pricing and gross margins are covered in 11; the cost of serving free users is in 10.