The risk that models will learn to evade monitoring probes (reward hacking or obfuscation) rather than changing their behavior.
The immaturity of 'intentional design' techniques, which are not yet considered safe for use on frontier AI models.
The inherent difficulty and vast complexity of fully understanding the internal workings of large neural networks.
Opportunities Identified
Using 'intentional design' to proactively build safer, more reliable, and more controllable AI systems.
Applying interpretability as a tool for scientific discovery, capable of auditing complex models and uncovering novel mechanisms in fields like healthcare.
Improving model performance and efficiency by identifying and removing non-essential components, such as 'memorization weights'.
Developing a commercial 'interpretability powered stack' for enterprise use in auditing, monitoring, and controlling AI models.