Economic Efficiency: Reduced costs for mid-tier professional services
Market Polarization: Premium human experts vs. AI systems structure
Enhanced Accessibility: Democratization of professional services through AI
Structural Transformation: Fundamental reshaping of professional service industries
Economic Impact
Winners: A+ Experts (strengthened monopolistic position), AI service providers, general consumers
Disrupted: Mid-tier professionals (Expert and Agent levels)
Market Change: Structural reorganization and pricing transformation in professional services
Conclusion
This diagram effectively demonstrates not just job displacement, but the economic restructuring of professional service markets, showing how AI-driven substitution leads to cost structure changes and market bipolarization.
A three-stage process: Sensing → Analysis → Decision
Clear distinction between Machine Role and Human Role
Classified as a “Small Automation Step”
Lower Section – Real-world Complex Process:
Multiple HITL steps connected in a workflow pattern
Human intervention points (red “S” markers) at each stage labeled “It Also Human Role (Decision)”
Forms an overall “Real Big Process”
Complex structure with cyclical feedback loops
Key Concepts
This diagram illustrates not simple linear automation, but a collaborative system where humans and machines work together. It demonstrates that human judgment and intervention are required at each stage, and these small units combine to form larger business processes.
This chart compares power consumption and cooling requirements for server-grade computing hardware.
CPU Servers (Intel Xeon, AMD EPYC)
1U-4U Rack: 0.2-1.2kW power consumption
208V power supply
Standard air cooling (CRAC, server fans) sufficient
PUE: 1.4-1.6 (Power Usage Effectiveness)
GPU Servers (DGX Series)
Power consumption and cooling complexity increase dramatically:
Low-Power Models (DGX-1, DGX-2)
3.5-10kW power consumption
Tesla V100 GPUs
High-performance air cooling required
Medium-Power Models (DGX A100, H100)
6.5-10.2kW power consumption
400V high voltage required
Liquid cooling recommended or essential
Highest-Performance Models (DGX B200, GB200)
14.3-120kW extreme power consumption
Blackwell architecture GPUs
Full liquid cooling essential
PUE 1.1-1.2 with improved cooling efficiency
Key Trends Summary
The evolution from CPU to GPU computing represents a fundamental shift in data center infrastructure requirements. Power consumption scales dramatically from kilowatts to tens of kilowatts, driving the transition from traditional air cooling to sophisticated liquid cooling systems. Higher-performance systems paradoxically achieve better power efficiency through advanced cooling technologies, while requiring substantial infrastructure upgrades including high-voltage power delivery and comprehensive thermal management solutions.
※ Disclaimer: All figures presented in this chart are approximate reference values and may vary significantly depending on actual environmental conditions, workloads, configurations, ambient temperature, and other operational factors.
This image titled “Machine Changes” visually illustrates the evolution of technology and machinery across different eras.
The diagram progresses from left to right with arrows showing the developmental stages:
Stage 1 (Left): Manual Labor Era
Tool icons (wrench, spanner)
Hand icon
Worker icon Representing basic manual work using simple tools.
Stage 2: Mechanization Era
Manufacturing equipment and machinery
Power-driven machines Depicting the industrial revolution period with mechanized production.
Stage 3 (Blue section): Automation and Computer Era
Power supply systems
CPU/processor chips
Computer systems
Programming code Representing automation through electronics and computer technology.
Stage 4 (Purple section): AI and Smart Technology Era
Robots
GPU processors
Artificial brain/AI
Interactive interfaces Representing modern smart technology integrated with artificial intelligence and robotics.
Additional Insight: The transition from the CPU era to the GPU era marks a fundamental shift in what drives technological capability. In the CPU era, program logic was the critical factor – the sophistication of algorithms and code determined system performance. However, in the GPU era, training data has become paramount – the quality, quantity, and diversity of data used to train AI models now determines the intelligence and effectiveness of these systems. This represents a shift from logic-driven computation to data-driven learning.
Overall, this infographic captures humanity’s technological evolution: Manual Labor → Mechanization → Automation → AI/Robotics, highlighting how the foundation of technological advancement has evolved from human skill to mechanical power to programmed logic to data-driven intelligence.
