The Supply Chain of Artificial Intelligence

The AI pyramid: Mapping the upstream supply chain architecture

The contemporary artificial intelligence ecosystem operates through a hierarchical supply chain structure characterised by extreme complexity, geographic concentration, and strategic interdependencies. This analysis examines the upstream value chainfrom intellectual property design to data centre deployment,revealing critical bottlenecks and geopolitical vulnerabilities that underpin modern AI capabilities.

Whilst much attention focuses on the downstream applications of artificial intelligence, the upstream supply chain represents the foundational infrastructure enabling AI advancement. This upstream architecture encompasses five distinct levels, each with unique actors, dependencies, and strategic implications for global AI development.

Level 5: Intellectual property and chip design architecture

• Core Function

  • The conceptual genesis of AI hardware begins with the design of processing architectures—graphics processing units (GPUs), tensor processing units (TPUs), and application-specific integrated circuits (ASICs). This level transforms theoretical computational requirements into implementable semiconductor blueprints.

• Key Actors and Market Positioning

  • NVIDIA maintains dominant market position through its GPU architectures (H100, B200 series), specifically optimised for parallel processing demands of machine learning workloads.

  • AMD provides primary competition in the GPU space,

  • whilst Google demonstrates vertical integration through proprietary TPU development.

  • ARM Holdings supplies fundamental instruction set architectures increasingly deployed across mobile and server environments.

• Critical Dependencies

  • Electronic Design Automation (EDA) software represents an essential but often overlooked dependency. Synopsys, Cadence Design Systems, and Siemens EDA provide the sophisticated tools required to translate architectural concepts into manufacturable designs. These platforms constitute critical chokepoints, as semiconductor design cannot proceed without access to advanced EDA capabilities.

Level 4: Manufacturing equipment and tooling infrastructure

• Core Function

  • This level encompasses the extraordinarily sophisticated machinery required for semiconductor fabrication, including lithography systems, deposition equipment, and etching tools operating at nanoscale precision.

• Strategic Market Analysis

  • ASML (Netherlands) maintains virtual monopoly over extreme ultraviolet (EUV) lithography systems—the only technology capable of producing the most advanced semiconductors. This concentration represents perhaps the most significant single point of failure in the global AI supply chain.

  • Applied Materials (United States) dominates thin-film deposition and surface modification equipment, whilst Lam Research (United States) leads in plasma etching systems. The geographic concentration within allied nations creates both security advantages and potential vulnerabilities through concentrated supply chains.

• Geopolitical Implications

  • The equipment level demonstrates the highest degree of technological concentration and export control sensitivity. Advanced semiconductor manufacturing equipment is subject to extensive international trade restrictions, creating potential for supply chain disruption through policy mechanisms.

Level 3: Semiconductor fabrication and foundry operations

• Core Function

  • Foundries transform intellectual property designs into physical silicon wafers through complex manufacturing processes involving hundreds of individual steps at sub-10-nanometer geometries.

• Market Structure and Capabilities

  • Taiwan Semiconductor Manufacturing Company (TSMC) maintains clear technological and capacity leadership, manufacturing for NVIDIA, Apple, AMD, and numerous other fabless semiconductor companies. Samsung Foundry (South Korea) provides secondary capacity with advanced process capabilities.

  • Intel represents a significant strategic development, transitioning from integrated device manufacturing toward foundry services whilst maintaining internal design capabilities. This shift reflects broader industry restructuring toward specialisation.

• Strategic Vulnerabilities

  • The foundry level exhibits extreme geographic concentration, with advanced capabilities concentrated in Taiwan and South Korea. This creates substantial geopolitical risk for global AI development, as evidenced by ongoing concerns regarding Taiwan's strategic position.

Level 2: Assembly, testing, and critical component integration

• Core Function

  • Individual semiconductor dies require packaging, testing, and integration with complementary components to create functional processing systems.

• Key Components and Suppliers

  • High Bandwidth Memory (HBM) represents a critical bottleneck, with SK Hynix and Samsung dominating production of these essential components for AI accelerators. Memory bandwidth constraints often limit AI system performance more significantly than processing capabilities.

  • Interconnection systems enable scaling beyond individual processors. NVIDIA's NVLink and Broadcom's networking solutions facilitate multi-GPU configurations essential for large-scale AI training.

• Assembly and Test Operations

  • ASE Group and Amkor Technology provide outsourced semiconductor assembly and test (OSAT) services, representing another layer of specialisation and geographic distribution within the supply chain.

Level 1: System integration and data centre infrastructure

• Core Function

  • The final physical integration combines processed semiconductors with supporting infrastructure to create deployable AI systems within data centre environments.

• System Integration Landscape

  • Supermicro, Dell Technologies, and Hewlett Packard Enterprise design and manufacture AI-optimised server systems. These companies must balance standardisation with customisation for specific AI workloads whilst managing thermal and power constraints.

• Infrastructure Requirements

  • Cloud service providers—Amazon Web Services, Microsoft Azure, Google Cloud Platform—represent the primary deployment environment for AI systems. These platforms must provide not only computational resources but also massive electrical power infrastructure, cooling systems, and network connectivity.

• Critical Resource Dependencies

  • Electrical power represents the fundamental constraint on AI system deployment. Training large language models requires sustained power consumption measured in megawatts, creating dependencies on electrical grid infrastructure and sustainable energy supplies.