57:24François Chollet argues that scaling deep learning alone will not deliver human-like general intelligence, which he defines as learning new tasks with human-level efficiency. At Indium, his team is exploring program synthesis and symbolic descent to find compact models that learn from less data and generalize better. He explains how verifiable rewards and post-training have driven progress in coding agents and ARC-AGI benchmarks, while warning that benchmark saturation can reflect engineering effort rather than greater fluid intelligence. ARC-AGI-3 tests whether agents can explore unfamiliar interactive environments efficiently, and Chollet predicts AGI could emerge around 2030. He also discusses building self-improving AI research, lessons from creating Keras, and how expertise can help people use AI progress to their advantage.