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Interpretability: The Critical Path to Safe Superintelligence

We stand at the threshold of creating systems with capabilities that will far exceed human understanding. But we cannot safely build what we do not understand.

I. Understanding trumps acceleration

Reverse-engineering neural computation reveals the algorithms and complexities that drive intelligence. By decoding these mechanisms, we can systematically and safely improve them rather than relying on blind scaling.

II. Interpretability transforms black-box evolution into directed design

Current AI progress resembles natural selection—powerful but inefficient. Interpretable training will enable us to engineer intelligence with the precision of designing a microchip.

III. Safety and capability are fundamentally linked

Systems we cannot understand will hit capability ceilings due to alignment failures. Only with truly interpretable systems can AI be safely pushed to theoretical limits.

Our Approach

I. We focus on a proof-based setting involving programming invariants and mathematical reasoning.

We have external measures of complexity that enable predictions about actual capabilities while others are limited to post-hoc explanations or weak statistical correlations. Pulling ground truth from formal logic means we are unbounded by conventional limitations on data availability. We can scale to arbitrary levels of difficulty or complexity by programmatically generating ground truth.

II. We build for real-world use from day one

Our interpretability tools are designed to be used by AI developers immediately, creating a virtuous cycle of research and application.

III. We are unlike other labs. Join Us.