Recent progress in artificial intelligence (AI) is powered by three key elements: algorithmic innovations, specialized chips and hardware, and a rich ecosystem of software and data toolboxes. This paper provides an analysis of these three key elements, tracing the evolution of AI from symbolic systems and small, labeled benchmarks to today’s large-scale, generative, and agentic models trained on web-scale corpora. We review the hardware trajectory from central processing units (CPUs) to graphics processing units (GPUs), tensor processing units (TPUs), and custom accelerators, and show how the co-design of chips and models has unlocked improvements in throughput and cost by orders of magnitude. On the algorithmic side, we cover the deep learning revolution, scaling laws, pretraining and fine-tuning paradigms, and multimodal and agentic architectures. We map the modern software stacks, i.e., open-source AI frameworks, end-to-end toolchains, and community datasets, that make model development reproducible and widely accessible. Given the environmental and infrastructural impact of scale, we emphasize the trade-offs in energy, datacenter, and governance. Finally, we identify emerging trends that reshape how AI is developed and deployed.