Signals

Microsoft's 2030 timeline for AI agents replacing SaaS is bold, but the governance implications are immediate. Success requires building accountability into agent architectures from the start.

OpenAI's decision to kill their chat discovery feature after users accidentally shared private conversations reveals why AI privacy design needs new approaches—not just better warnings.

WeTransfer's quick reversal on AI training rights shows how standard legal approaches to AI can instantly undermine brand trust—even for privacy-conscious companies that should know better.

Research shows 250 poisoned documents can backdoor any size language model—challenging the assumption that larger models need proportionally more malicious data. The fixed-number threshold changes how teams should think about data provenance and vendor due diligence.

Product counsel has focused on policy design, but Fast Company research reveals that 31% of employees actively sabotage AI strategies. The real challenge is navigating identity crises.

Anthropic's Humanloop acquisition shows enterprise AI competition has moved beyond models to specialized talent who can build trust into AI systems.

MIT research shows how single-word changes can flip AI text classifier decisions. The testing gap affects content moderation, financial services, and medical AI systems across production environments.

Multi-agent workflows create distributed accountability that challenges existing legal frameworks designed for single AI tools rather than collaborative AI systems with emergent behaviors and shared decision-making.