Noam Brown asks how to trust AI before it improves itself
Noam Brown’s interview connects multi-agent systems to the harder question ahead: proving models are aligned before recursive self-improvement begins. It moves from AI agents tackling hard mathematics, including a system described as 10,000 agents using 130 billion tokens over 88 hours, to the gap between a model’s internal reasoning and its outward behavior. The practical test is whether we can trust models as they help automate AI research.