The LLM foundation model era has inverted a fundamental assumption in software engineering. Code, once written, no longer belongs exclusively to its creators. Any publicly accessible code becomes training data, absorbed into models that can reproduce, adapt, and redistribute it without consent. This paper argues that such circumstances represent not merely a legal or ethical challenge, but a technical one requiring new defensive primitives. Without technical defenses, the most valuable software becomes training data for systems its authors do not control. We introduce the concept of Statistical Opacity, defined as the deliberate design of code representations that resist neural pattern extraction while preserving human readability and machine executability, by exploiting the gap between executability and learnability. We articulate a research agenda spanning theory, pathways, tools, and evaluation.