# public trust

Public confidence in artificial intelligence remains low while flagship economics and field results raise hard questions about the durability of the current model. Massive reported losses, underperforming enterprise deployments, and fresh security exposures suggest that strategy must shift toward data quality, architecture choices, and measurable outcomes.

Compliance decisions are shaping product availability, while debates over water, energy, and siting signal that infrastructure and permitting will determine the pace of scale. At the same time, authenticity risks and hostile street-level reactions show that public trust, not only model capability, will govern adoption, pushing builders toward auditability, memory, and resource-aware design.

Market signals are diverging as an 11% drop in Oracle's shares underscores capex and demand concerns while a $1 billion commitment from Disney to OpenAI highlights continuing strategic bets on generative content. Simultaneously, widening trust gaps, mounting safety failures, and a push for a national AI framework are reshaping the social license and regulatory playbook. Labor norms are shifting as automation displaces workers and AI-driven hiring practices redefine evaluation and dignity at work.