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.

Andrusko's a16z analysis reveals why "thinking partner" rhetoric clashes with billable hour economics. Since AI tools will never be flawless, traceability and workflow adaptation matter more than sophistication

TL;DR: The rapid deployment of agentic AI systems across organizations has created an urgent need for comprehensive traceability and auditability fram…

Anthropic's safeguards architecture demonstrates how legal frameworks transform into computational systems that process trillions of tokens while preventing harm in real-time.

Working with summer interns revealed that the next generation treats AI as another developing tool, not an existential threat.

Justice Kagan's surprise at Claude's constitutional analysis reveals an irony: while we fixate on AI hallucinations, we miss when machines reason more systematically than humans, modeling dispassionate legal analysis.

Legal AI adoption isn't just about efficiency gains—it's about positioning for a market where early adopters build compounding advantages that become nearly impossible for late adopters to overcome.

Altman's admission about ChatGPT's confidentiality problem exposes a fundamental design flaw: AI systems that encourage professional-level trust without professional-level legal protections.