The rapid growth of AI is driven by a feedback loop involving algorithmic breakthroughs, hardware improvements, data availability, and increased funding. New architectures and powerful GPUs enhance model training, attract more investments, and fuel further advancements. This cycle has accelerated the development of foundation models that are increasingly capable. Before the rise of deep learning, AI relied on symbolic systems that used explicit symbols and rules. While these systems showed promise in specific areas, they faltered due to their inability to connect these symbols to real-world perceptions, leading to brittleness and failures. As tasks became more complex, the effort required to hand-code rules slowed progress, leading to an AI "winter." The shift to learned representations through deep learning allowed engineers to bypass manual rule creation and instead derive high-dimensional representations directly from data. Key algorithmic advancements, particularly after 2017, have transformed AI capabilities. OpenAI’s work with reinforcement learning demonstrated that systems could learn complex strategies in multi-agent environments at scale. The introduction of transformer architectures marked a significant leap, allowing models to capture long-range dependencies in data efficiently. OpenAI’s GPT models illustrated the power of scaling laws, showing how increasing model size and training data leads to better performance. Techniques like reinforcement learning from human feedback, transfer learning, and mixture of experts further optimized these models. Simultaneously, advancements in software and hardware infrastructure were essential for scaling AI training. OpenAI’s development of Kubernetes clusters, which expanded to thousands of nodes, allowed for large-scale experiments without infrastructure constraints. Their internal tools, such as Rapid, facilitated high-throughput training jobs, supporting major projects like GPT-3 and DALL·E. This combination of algorithmic innovation and robust infrastructure has enabled rapid advancements in AI capabilities.