Ken Priore

The most practical insight involves accepting that perfect solutions do not yet exist. Traditional agency mechanisms provide valuable frameworks for identifying problems and structuring solutions; however, new technical and legal infrastructure must evolve in tandem with the technology.

The shift to widespread AI requires a shift in approach: from reactive problem-solving to intentional design... The organizations that get ahead of this will be the ones that can prove their AI systems work as intended—and can be trusted accordingly.

Research shows that teams optimizing for collaborative patterns—not individual star talent—consistently outperform, offering essential guidance for AI teams navigating rapid technological change.

The goal here isn't to slow down these hyper-efficient teams, but to demonstrate that robust legal design can be a competitive advantage in the AI economy, enabling sustainable growth rather than constraining it.

OpenAI's CEO says we're heading for an AI fraud crisis, and the authentication methods most companies rely on are already broken.

Ruth Porat's human-in-the-loop mandate gives product teams a concrete framework for building agentic systems that users will trust and regulators will accept.

VCs are backing companies that use AI to achieve software margins in service businesses, but research shows 40% of workers spend extra time fixing low-quality AI output that eats the projected gains.

MIT researchers tested GPT and ERNIE in English and Chinese. The finding: language choice shapes the cultural assumptions in AI responses. When prompted in English, models reflected American values. In Chinese, they shifted to Chinese values.