Why it matters

Capability without visibility creates a widening control gap. We look for infrastructure that turns opaque model behavior into evidence: what the system can do, where it fails, how it responds to adversarial conditions and whether its behavior remains stable after deployment changes.

uncertainty ↓   ·   evidence ↑

Assurance becomes infrastructure

For frontier and enterprise AI alike, a one-time evaluation is not enough. Models gain tools, data access, memory and new deployment contexts. The relevant market therefore extends from pre-deployment evaluation into continuous monitoring, adversarial testing and technical assurance.

  • Capability evaluations and benchmarking
  • Interpretability and behavior analysis
  • Red teaming and adversarial testing
  • Runtime monitoring and evidence capture
  • Formal and semi-formal verification methods

What we invest in

We are interested in products that make safety evidence operationally useful: faster deployment decisions, stronger access controls, better incident response or clearer accountability for high-impact systems.

Discuss this thesis ↗