The concept of technical debt is evolving in the AI era, where failure modes are more complex and subtle. Traditional technical debt, characterized by outdated architecture and poorly maintained code, is no longer sufficient to describe the risks associated with AI systems. The introduction of AI has led to new layers of technical debt, including prompt debt, retrieval debt, and evaluation debt. These types of debt are less visible and harder to measure than traditional debt, making them more insidious and potentially more damaging. The complexities of AI systems and their associated failures have been well-documented, with studies showing that a significant majority of AI projects fail to reach production or deliver value. The quiet reshaping of enterprise AI risk by these new types of debt is a crisis hiding in plain sight. As AI continues to play an increasingly important role in business, understanding and addressing these new types of debt will be crucial to mitigating risk and ensuring the success of AI projects.

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