AI infrastructure is a strategic asset that determines the technological sovereignty and competitiveness of nations and corporations.
# Technical perspective: A system that supports AI models to perform learning and inference processes reliably and at scale, integrating elements such as:
- Hardware (e.g., GPUs, cloud servers)
- Software (e.g., deep learning frameworks)
- Data (e.g., training and evaluation datasets)
- Networks
# Comprehensive perspective: Not merely a collection of technical components, but a complex system tightly connected to the various conditions required for it to operate in the real world.
No matter how advanced high-performance computing devices, large-scale data, or sophisticated algorithms are, AI cannot operate sustainably in real-world environments without the necessary physical and social foundations.
AI is not self-contained; it materializes only through interactions with diverse domains such as resources, energy, space, institutions, policies, and ethics.