Programming in Assembly Is Brutal, Beautiful, and Maybe Even a Path to Better AI (www.wired.com)

The article revisits assembly language through the lens of craft and efficiency—using Chris Sawyer’s solo creation of RollerCoaster Tycoon (entirely in x86 assembly) as a vivid example of how low-level coding forces intimate knowledge of hardware. Assembly maps almost directly to a CPU’s fetch-decode-execute cycle, registers, and gates, demanding precise, brittle but explainable instructions. That discipline produced hyper-efficient software in an era of scarce compute, and the piece argues that mindset still matters even as high-level languages dominate. For AI/ML the takeaway is practical: hardware-aware, low-level thinking can unlock large efficiency gains. The article highlights two modern examples—DeepSeek, a Chinese team that tweaked Nvidia hardware behavior to compress 32-bit operations to 8-bit at critical moments for major efficiency wins, and DeepMind researchers who taught a model x86 assembly to optimize a standard sort() routine, shaving micro-steps that scale across runs. These efforts show that quantization, chip-specific tricks and ML-driven assembly-level optimization are viable levers to reduce energy and latency. In short, revisiting “assembly thinking” and co-design between models and hardware can be a fruitful path toward more efficient AI.