AI Infrastructure Race Accelerates: From South Korea's $1 Trillion Investment to Data Center Cooling and Full-Stack Strategy

인프라/플랫폼 | Tue Jun 30 2026 00:00:00 GMT+0000 (Coordinated Universal Time) | 5 sources

Movements across AI infrastructure including South Korea's massive investment in memory and data centers, Google's full-stack AI strategy, and innovations in data center liquid cooling.

Analysis

[South Korea] announced a $1 trillion national investment plan for memory chips and AI data centers ^[2]^[4]

Samsung and SK Hynix to invest $518 billion in building four new memory fabs in the southwestern region
An additional $52 billion allocated to an HBM packaging hub in the central region
SK
GS
Naver
and others to invest $356 billion in AI data centers by 2035
Defined semiconductors
physical AI
and AI data centers as the 'three pillars'

[South Korea] set a 2028 commercialization target for physical AI and humanoid robots ^[4]^[2]

Hyundai Motor pushing for mass production of humanoid robots through its subsidiary Boston Dynamics
Plans to deploy robot workers in workplaces such as automotive factories
Goal to double DRAM production within five years
Facing labor union pushback against humanoid adoption

[Google Cloud] explained the significance of its full-stack AI approach ^[1]

Combines every layer from hardware to models to UI into one integrated system
Eliminates the burden of integrating multi-vendor components
improving reliability and reducing cost
Provides Google AI Studio for prototyping
Provides Gemini Enterprise Platform for automation
Provides Antigravity platform for complex agent development

[Omen AI] launched a compact spectrometer for monitoring data center liquid cooling and raised a $31 million Series A ^[3]

Led by Nava Ventures
with participation from CRV
Vanderbilt University
and others
Detects bacterial growth in coolant in real time
Diagnoses pump wear by detecting copper and chromium
and seal wear by detecting silicon
Prevents losses of millions of dollars from rack shutdowns

[Apple Neural Engine] published a technical report reverse-engineering the ANE architecture and runtime ^[5]

Analyzes A11 through A18 and M1 through M5 chip families
Documents undisclosed dispatch paths beneath Core ML
Includes analysis of the compiler
weight compression schemes
kernel drivers
and firmware
Direct measurements performed on M1 and M5

AI Infrastructure Race Accelerates: From South Korea's $1 Trillion Investment to Data Center Cooling and Full-Stack Strategy

Analysis

Sources