Build vs Buy in the Age of AI
One topic that has been around since the beginning of the tech industry, is whether we should build or buy in order to solve some problem? This question applies to traditional IT, as well as to every product team. There are often one or more buy alternatives, but each comes with an associated cost,
- Source
- SVPG (Marty Cagan)
- Category
- Product Launch & Strategy
- Format
- Article
- Published
- August 19, 2025
Summary
This SVPG article examines how AI-powered development tools are reshaping the traditional build vs. buy decision framework that product teams have long navigated. Historically, companies would build solutions within their core competency and buy everything else, but the emergence of user-programming tools—from early spreadsheets to modern AI-powered platforms like Lovable and Bolt—now enables non-technical users to create applications using natural language as the programming interface.
Marty Cagan argues that despite predictions that AI tools will eliminate the need for SaaS solutions, enterprise software vendors aren't likely to disappear. The critical insight is that business applications contain thousands of complex business rules and millions of lines of business logic covering compliance, security, legal, and financial constraints. Most non-technical users lack awareness of these intricate requirements, which represent the domain expertise that product managers typically acquire to define viable solutions.
The proposed future model suggests a "yes to both" approach where companies will continue purchasing complex component services while also building custom solutions on top of them. The Model Context Protocol (MCP) enables this by creating a standard way for both humans and AI agents to interact with business services. This hybrid approach allows organizations to leverage the robustness of established SaaS platforms while gaining the flexibility of custom AI-generated solutions.
**Key takeaway for PMs**: As AI democratizes development capabilities, product managers' value increasingly lies in understanding complex business rules and requirements rather than just coordinating technical implementation. The ability to bridge business logic with technical solutions becomes even more critical in an AI-enabled environment.