In April 2023, Alexandr Wang, CEO of Scale AI, faced a pivotal decision that would shape the future of his company. Having achieved remarkable success in providing labeled data for autonomous vehicles (AVs), Scale AI was at a crossroads as generative AI gained traction. The company’s AV business was stable but showed signs of slower growth due to industry consolidation and advancements in simulation technologies. Meanwhile, the nascent market for large language models (LLMs) and reinforcement learning from human feedback (RLHF) offered immense, albeit uncertain, growth opportunities. The case chronicles Scale AI’s journey from its origins as a data-labeling solution for AVs to its exploration of new frontiers in enterprise, federal, and generative AI markets. As Wang evaluated four strategic options—doubling down on AV, expanding into enterprise AI, pursuing federal contracts, or pivoting to LLMs—the trade-offs became evident. The case explores the operational, financial, and cultural implications of each path, emphasizing the difficulty of reallocating resources and managing stakeholder expectations. Students are challenged to assess the strategic, organizational, and ethical considerations behind Wang’s decision, ultimately determining how Scale AI can maintain its position as an industry leader while embracing new opportunities.
