The foundational research "Why Probabilistic AI is Negligent and Uninsurable," translated for humans. We explain how floating-point non-associativity and batch variance create hardware-level race conditions inside AI guardrails, meaning a model asked to police itself can return different answers to the same question. From there we connect the physics to the law and the actuarial tables: why self-policing AI fails the standard of care, why insurers cannot price it, and what the Bitwise Standard does about it.
In this episode
- The dirty secret of floating-point math: why the same input can yield different safety verdicts
- Batch variance and race conditions in guardrails, explained without a math degree
- Why "the model checks itself" is a legal and actuarial dead end
- How the Bitwise Standard makes AI behavior deterministic, and therefore insurable
Based on our study
Why Probabilistic AI is Negligent and Uninsurable
Read it