Explore the implications of scaling up AI systems in this thought-provoking interview with Jacob Steinhardt. Delve into the concept of emergence in AI, examining how qualitative differences arise as systems scale up, using GPT-3 as a prime example. Investigate the intersection of engineering and philosophical perspectives on AI's future, and critically analyze the Paperclip Maximizer thought experiment in relation to AI safety and alignment. Examine potential failure modes in machine learning systems, including imitative deception and the generalization of empirical findings. Gain insights into current research on AI truthfulness and deception, and consider recommendations for improving AI alignment and safety as these systems continue to evolve and emerge in unexpected ways.