What Happens When AIs Work Together?

METR researchers Chris Painter and Joel Becker discussed AI’s evolving autonomy in a recent podcast interview, emphasizing the need to measure models’ ability to perform complex tasks without human intervention. The organization focuses on developing metrics to assess AI systems’ capacity for independent problem-solving, particularly in high-stakes scenarios where reliability is critical. Painter highlighted that current AI benchmarks often fail to capture real-world adaptability, requiring new evaluation frameworks to test collaborative and autonomous decision-making. Becker noted that multi-agent AI systems—where models work together—could unlock unprecedented capabilities but also introduce unpredictable risks if not properly governed. The discussion underscored the urgency for standardized testing protocols to ensure safe, scalable AI deployment as models grow more sophisticated and interconnected.