The Architecture of AI: Mapping the Layers That Shape the Industry

AI is often viewed through the lens of applications, chatbots, image generators, and recommendation systems, but its true structure lies deeper. Beneath the surface is a layered architecture of hardware, infrastructure, models, and capital flows that determines where value and power concentrate. 

This article explores that architecture, how compute, manufacturing, and supply constraints shape the pace and direction of AI’s growth. It examines: the Layered Architecture of AI, the Hardware Foundation, the role of Fabs and Foundries, and the Strategic Implications for those building within this evolving ecosystem. Over time, as compute becomes ubiquitous and inexpensive, AI will integrate into every sector much like electricity, pervasive, invisible, and indispensable. 

This ecosystem is still forming, setting the foundation for an eventual embedding of AI into every commercial and consumer context. The process may unfold over the next two decades, as inference becomes as ubiquitous as electricity, invisible, cheap, and everywhere. 

Source: The Business Engineer (2025)

The Layered Architecture of AI 

To understand who holds leverage in AI, one must look across its layered stack, from hardware foundations to the applications built on top.  

Each layer, from infrastructure to models and user-facing products, plays a distinct role in shaping performance, scalability, and value capture across the ecosystem. The modern AI stack is organized into interconnected layers, each representing a distinct source of value creation and competitive advantage: 

• Hardware Layer: The foundation of AI performance. Chips such as GPUs, TPUs, NPUs, and ASICs enable large-scale computation for training and inference. 

• Cloud & Compute Infrastructure: Scalable environments from hyperscalers like AWS, Google Cloud, and Azure that provide the compute backbone for model training. 

• Model Layer: Foundation and domain-specific models, from general-purpose LLMs (GPT, Claude, Gemini) to fine-tuned vertical models, that define performance boundaries. 

• Vertical & Consumer Applications: Industry-specific AI solutions (finance, healthcare, manufacturing) and consumer products that bring AI directly into daily workflows. 

• AI-Integrated Hardware: Devices such as smart glasses, wearables, and edge systems that embed intelligence locally, closing the loop between physical and digital interaction. 

Source: The Business Engineer (2025) 

Each layer compounds upon the one below it. Competitive advantage often emerges at the intersections, where infrastructure meets application, or where proprietary data enables model differentiation. 

For most companies, operating in one or two layers offers focus and defensibility. Only a handful such as Google, OpenAI, Microsoft, Amazon, and Meta,  attempt multi-layer integration to build end-to-end moats.’ 

Hardware: The Foundation of the Stack 

The hardware layer defines the ceiling for AI performance. It integrates four critical subsystems, compute, memory, interconnect, and workload optimization, that together determine the efficiency, scalability, and economics of AI at scale. Below is a focused look into the primary subsystems and how they coalesce.  

Source: The Business Engineer (2025) 

Compute Units 

Each compute unit is tuned for a particular balance of throughput, latency, and efficiency. Compute lies at the core of AI workloads: 

• GPUs & TPUs: General-purpose GPUs are well-suited for large-scale training. TPUs and tensor-optimized ASICs tailor compute paths for deep learning primitives (e.g. matrix multiplications). 

• NPUs & AI Accelerators: These are optimized for inference, especially at the edge. NPUs often operate on quantized representations with lower power draw. 

• Hybrid / Chiplet Designs: Modern architectures mix compute types (CPU + GPU + NPU) on a single package, connected through local interconnects. 

Memory & Data Access 

Efficient memory hierarchy and data orchestration unlock the full potential of compute units.  Feeding these compute engines demands a sophisticated memory pipeline: 

• High-Bandwidth Memory (HBM): Close-coupled DRAM stacks that offer wide data paths and low latency, essential for heavy workloads. 

• On-Chip Caches / SRAM: Fast local storage that stages data and alleviates pressure on external memory. 

• Near-Memory / In-Memory Processing: Emerging designs embed compute near memory banks to reduce movement cost and energy. 

• Memory Fabrics: In large systems, memory may be shared or disaggregated across units, forming a fabric of data access. 

Interconnect & Fabric 

Interconnect design balances bandwidth, latency, coherence, and scalability.  Compute and memory must be wired together with minimal friction: 

• Network-on-Chip (NoC): On-chip routing layer that connects tiles, caches, and accelerators. 

• High-Speed Links / Protocols: Between chips, protocols such as NVLink, CXL, or proprietary fabrics enable high-bandwidth, low-latency communication. 

• Cluster Fabric / Pod-Scale Networks: At rack or cluster scale, cross-node interconnects become critical to coordinate distributed compute. 

Unified Trade-offs & Use Cases 

Good designs achieve hardware–software co-optimization: compiler, scheduler, data layout, and compute architecture must align to deliver consistent gains. When integrated, these subsystems support two core modes: 

• Training Workloads: Maximize parallelism and throughput , memory bandwidth and compute are priority, while occasional inefficiencies in latency or power are tolerable. 

• Inference Workloads: Demand tight latency bounds, energy efficiency, and predictable performance, often near the data source. 

Fabs and Foundries 

While most discourse around AI focuses on chips, models, and cloud, the manufacturing backbone, semiconductor fabs and foundries, is poised to become the silent pivot of the entire value chain.  We examines the scale of global investment, the strategic importance of fabrication in AI competitiveness, and the structural challenges that will shape how nations and companies build this critical infrastructure. 

The Scale of Investment & the Global Surge

According to Mckinsey (2025), the industry is targeting $1 trillion+ in fab investments through 2030 to support next-generation semiconductor capacity.  

• Strategic commitments are already underway: GlobalFoundries (2025) announced a $16 billion U.S. investment to expand its chip manufacturing footprint. 

• National and regional policies, for instance, the U.S. CHIPS Act, are fueling incentives to onshore advanced node fabs, reduce supply-chain risk, and assert sovereign control over critical infrastructure.  

  
  

Why Fabs Matter in AI Strategy 

• Bottleneck Leverage: As AI chips demand tighter tolerances, advanced lithography, and novel packaging (e.g. 2.5D, 3D stacking, chiplets), having fabs that push manufacturing boundaries unlocks a deep moat. 

• Sovereignty and Security: Controlling the fabrication layer reduces dependencies on geopolitical chokepoints, export controls, or supply disruption. 

• Co-innovation & Differentiation: When a company designs chips and simultaneously co-owns or controls fabrication, it can exploit process-level optimizations unavailable to external clients. 

• Integration across Edge-to-Cloud: Edge AI will drive demand for specialized nodes, custom packaging, and hybrid architectures. Designers deeply aligned with manufacturers will capture the premium in performance and power efficiency. 

  
  

Challenges & Strategic Trade-offs 

• Massive CapEx & Long Time Horizons: Building a leading-edge fab costs multiple billions and often takes 5–10 years to reach stable production, risking obsolescence if technology shifts midstream. 

• Talent, Yield, and Complexity: Advanced nodes require deep expertise, near-flawless yields, tight process control, and years of iteration. 

• Energy & Infrastructure Demand: Fabs demand massive power, water, clean-room infrastructure, and cooling systems. The expansion will often run parallel to energy and utility upgrades.  

• Geopolitics & Trade Risk: Fabs straddle trade policies, subsidy regimes, cross-border supply contracts, and national security constraints. 

 

Implications: Competing Along the AI Value Chain 

For founders, understanding the AI value chain isn’t just academic, it’s strategic. Each layer, from silicon to software, defines a different source of leverage. Knowing where to play, and how deep to integrate, determines whether a company builds a durable moat or becomes dependent on others’ infrastructure.  

We will outline three dimensions of competitive advantage: how to position within the stack, how to manage ecosystem interdependence, and how capital and timing shape long-term outcomes. 

Strategic Positioning 

Strategic focus determines durability within the AI stack: 

• Pick your depth, not just your niche. In the AI stack, focus is power. Competing across too many layers dilutes capital and talent. Excelling in one or two, and mastering the interfaces between them, yields more defensibility than chasing vertical integration. 

• Defensibility moves downward. As models and APIs commoditize, differentiation migrates toward data, infrastructure control, and real-world deployment. Owning the bottleneck, whether compute access, proprietary datasets, or on-device presence, shapes long-term advantage. 

  
  

 Ecosystem Dependence 

Interconnectedness across the stack makes collaboration essential. 

• The stack is interdependent. A startup’s success in one layer often hinges on alignment with players above and below, chip suppliers, cloud providers, or application distributors. Building partnerships early can offset dependence and reduce scaling risk. 

• Hardware awareness is now a founder skill. Even software-native teams must understand compute economics. Access, latency, and cost will increasingly shape product feasibility and gross margins. 

  
  

Capital and Timing 

Capital allocation and market timing shape competitive outcomes. 

• Follow the capital flow. The next decade will see trillions funneled into fabs, energy, and compute, reshaping cost curves and regional dynamics. Startups that anticipate where capacity will expand (and where it won’t) can position themselves ahead of bottlenecks. 

• Timing the layer shift. As AI infrastructure matures, opportunities will cascade upward: first in compute efficiency, then model differentiation, then applied intelligence. Founders who time their entry at the right inflection point, when a lower layer stabilizes, can ride the next wave of abstraction. 

  
  

The Takeaway 

The AI economy rewards those who understand the stack as a system, not a buzzword. Whether you build models, applications, or tools, your defensibility depends on how you connect to, or control, the layers beneath you. 

Over the next decade, the real question isn’t “What’s your AI feature?” It’s “Where in the value chain do you create non-replaceable value, and who controls your bottleneck?” 

List of references: 

Cuofano, G. (2025, March 17). The AI value chain. The Business Engineer. https://businessengineer.ai/p/the-ai-value-chain 

GlobalFoundries. (2025, June 4). GlobalFoundries announces $16B U.S. investment to reshore essential chip manufacturing and accelerate AI growth. GlobalFoundries. https://gf.com/gf-press-release/globalfoundries-announces-16b-u-s-investment-to-reshore-essential-chip-manufacturing-and-accelerate-ai-growth/?utm_source=chatgpt.com 

Pilling, D., & Steele, M. (2025, August 6). Unleashing AI’s next wave of infrastructure growth. Sands Capital. Retrieved from https://www.sandscapital.com/unleashing-ais-next-wave-of-infrastructure-growth/