Science ventures do not behave like startups. They evolve through complex dependency chains, long timelines, non-linear progress, and irreversible validation events. This article explains the core system logic governing science venture systems, showing why misclassification distorts capital allocation, evaluation, and governance. It introduces system-aware reasoning to align investment, strategy, and policy with how scientific value is actually created.

When deep tech is evaluated with startup terminology, metrics, and funding logic borrowed from software, misclassification becomes inevitable. This article shows how vague tech language distorts evaluation, capital allocation, and strategy at scale, explains why high tech, deep tech, and tough tech require different logics, and argues that precision in language is a structural advantage for institutions, investors, and innovation systems.

Science innovation is systematically misjudged when evaluated through startup and software logic. This article explains why science ventures are a distinct category with different timelines, risks, and capital dynamics, and how misclassification distorts metrics, incentives, and investment decisions. It outlines the hidden economic cost of applying startup frameworks to scientific innovation and why correct categorization is the foundation for sound investment, policy, and ve