AI Models as Depreciating Assets
Definition
The concept that unlike standard software which appreciates through network effects and code maturity, AI models act more like heavy machinery that depreciates over time due to data drift, concept drift, and changes in the deployment environment.
Why It Matters
Executives often capitalize AI initiatives as if they are standard software. However, preventing model collapse and maintaining safe execution requires massive ongoing OPEX. Treating AI as a depreciating asset forces proper lifecycle accounting, ensuring continuous safety checks and infrastructure investment.
How Exogram Addresses This
Because models depreciate, their outputs become less trustworthy over time. Exogram's deterministic execution boundary is immutable. It does not depreciate. It provides a stable safety floor regardless of how the underlying reasoning engine degrades or drifts over time.
Is AI Models as Depreciating Assets vulnerable to execution drift?
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Related Terms
Key Takeaways
- → This concept is part of the broader AI governance landscape
- → Production AI requires multiple layers of protection
- → Deterministic enforcement provides zero-error-rate guarantees