The cost of saying yes has changed

The latest thinking from GitHub Engineering offers a critical framework for distinguishing genuinely cheap changes from those that accrue hidden costs in the age of AI-assisted development. This piece fundamentally argues that while large language models and other AI tools have dramatically reduced the effort required to produce new code, they have not similarly lowered the long-term burden of maintenance, security, and operational overhead associated with that code. The core contention is that the perceived “cheapness” of AI-generated deliverables often masks significant future expenses related to ownership, necessitating a re-evaluation of development priorities and decision-making processes. This insight directly impacts how teams decide what to build, what to integrate, and where to invest their engineering resources. For an indie SaaS founder based in Austin, Texas, considering a complex