Reflections are deeper dives into how law, technology, and innovation intersect. These longer form pieces analyze research and emerging trends — offering perspectives that help teams navigate what's coming next.
Credo AI's Unified Control Framework maps 42 controls to multiple risks and regulations simultaneously, reducing governance fragmentation through bidirectional mappings and concrete implementation guidance.
After Stanford Law's agentic AI program, it was clear: companies are building autonomous capabilities faster than they can deploy them responsibly. This is part of a series exploring organizational frameworks that can keep pace with AI autonomy, which emerged from that program.
TL;DR: The rapid deployment of agentic AI systems across organizations has created an urgent need for comprehensive traceability and auditability fram…
MIT study of 2,310 participants reveals AI collaboration increases communication 137% while reducing social coordination costs, creating new opportunities and risks for product teams.
Apollo Research documents how AI companies deploy advanced systems internally for months before public release, creating governance gaps with serious competitive and legal implications requiring new frameworks.
University of Washington framework argues AI agent autonomy should be a deliberate design choice separate from capability, proposing five user role levels from operator to observer.
AI agents are shifting from copilots to autopilots, and Noam Kolt warns their speed, opacity, and autonomy demand governance rooted in inclusivity, visibility, and liability—urgent work for product and legal teams before regulation arrives.
The intersection of AI agents and enterprise accountability fascinates me, particularly the challenge of building systems that can operate autonomously while maintaining complete audit trails and decision traceability.
