OpenAI's Frontier team has completed an ambitious five-month experiment building and shipping an internal product with entirely AI-generated code, marking a significant milestone in autonomous software development. The project, led by Ryan Lopopolo, resulted in a codebase exceeding one million lines of code across thousands of pull requests, all written by Codex agents without human authorship or pre-merge review. The team deliberately avoided writing code themselves, forcing the AI system to handle end-to-end development tasks and establish what Lopopolo calls "harness engineering"—optimizing entire workflows and organizational structures around agent legibility rather than human habits. The initiative produced Symphony, described as a "ghost library" and reference implementation in Elixir that orchestrates multiple Codex agents through extensive system prompts structured like professional specification documents. Rather than iterating on agent prompts when failures occurred, the team investigated what capabilities, context, or structural elements were missing, fundamentally changing how they approached AI-driven development. Lopopolo argues that organizations consuming fewer than 1 billion tokens daily—roughly $2,000-3,000 in daily spending—are negligent in their AI adoption, suggesting the economics and capabilities of AI-native development have reached a critical inflection point. The experiment reveals that the bottleneck in enterprise AI development has shifted from token availability to human attention and oversight. With agents operating autonomously across codebases, the focus has moved to establishing robust observability systems, clear specifications, and skill frameworks that allow models to understand both technical requirements and organizational context. This work signals a transition where software must increasingly be written for machine comprehension alongside human understanding, positioning AI agents as full teammates rather than coding assistants.