This report explores a shift in AI development toward dynamic intelligence, pluralistic alignment, and non-agentic safety. Rather than relying on static benchmarks, experts redefine intelligence as a model's capacity to adapt to novel problems and complex reasoning tasks. To address human diversity, researchers propose jury learning, a method that preserves conflicting values and collective disagreements instead of forcing a singular, "correct" perspective. Safety concerns regarding instrumental self-preservation in frontier models have prompted the concept of Scientist AI, a framework designed to be purely epistemic. This approach prioritizes probabilistic prediction over goal-directed agency to prevent strategic manipulation by AI systems. Ultimately, these perspectives advocate for structural humility, where models understand human complexity without exerting uncontrolled or autonomous influence.
