As AI workloads evolve toward inference and agentic AI, the criteria for system performance are also changing. Computing power alone is no longer enough. Where data is stored, how quickly it moves, and how efficiently it is processed now have a direct impact on the performance and efficiency of AI systems.
This article series explores the transformation of the AI ecosystem, where software, data center infrastructure, semiconductors, and memory technologies work together. It also examines why memory semiconductors have become a foundational technology for the AI era and outlines SK hynix’s vision for building the next-generation AI memory ecosystem.
① The paradigm shift in AI computing – Professor Jinwoo Shin, KAIST
② The real bottleneck: Data, not compute
③ Redesigning infrastructure: Why architecture determines performance
④ The semiconductor paradigm shift: Memory’s growing role
⑤ Completing AI: Physical intelligence and the role of memory