Introduction: The Five-Layer Cake of AI
- Michael McClanahan
- 2 hours ago
- 6 min read
Artificial intelligence is often discussed as if it were a single product: Something we “buy,” “install,” or “turn on.” AI is not a feature. It is an ecosystem. It is an interconnected stack of capabilities that begins far below the surface of your organization and extends to your customer experience.
Recently, Jensen Huang, NVIDIA's CEO, described the AI ecosystem as a five-layer cake at the 2026 DAVOS conference.
These layers include:
Energy
Chips and Computing
Cloud Data Centers
AI Models
Industry Applications
Each layer builds on the one beneath it. Each layer has its own economics, risks, and strategic implications. And each layer shapes what is realistically possible for your business.
This first blog in the seven-part series introduces the five layers, explains how they interact, and provides practical planning considerations for business leaders preparing to implement AI, whether through external partnerships or internal capability building. The goal is not to dive into technical architecture, but to frame AI as a strategic stack that must be understood before it can be governed, scaled, or trusted.
Why the Layered View Matters
Many organizations approach AI from the top layer down. They begin with a chatbot, a predictive dashboard, or an automation tool. But a sustainable AI strategy works from the bottom up.
If the infrastructure is fragile, the models will be constrained. If the cloud architecture is poorly designed, the applications will stall. If the energy exposure is unexamined, the operating costs may surge unexpectedly.
Understanding the five layers allows executives to:
Align AI ambition with operational reality
Avoid automation bias driven by vendor marketing
Build resilience into AI adoption
Make informed contracting decisions
Recognize where competitive advantage resides
AI is not magic. It is engineered. And engineering has layers.
Layer 1: Energy — The Invisible Foundation
AI begins with electricity.
Modern AI systems, especially large-scale models, consume substantial amounts of power. Training a sophisticated model requires enormous computational effort, and running it at scale requires constant energy flow. Behind every “intelligent” system are power grids, cooling systems, and physical infrastructure.
Most businesses do not control energy generation. But they are affected by it.
Why Energy Matters Strategically
AI increases demand for compute-intensive workloads on demand
Data centers consume significant electricity
Sustainability goals intersect directly with AI adoption
Energy volatility affects cloud pricing and operational costs
For organizations expanding AI capabilities, energy is not just an environmental discussion. It is a cost, risk, and resilience issue.
Practical Planning Actions
Include energy cost sensitivity in AI budgeting: Understand how energy volatility may affect cloud service pricing.
Align AI strategy with ESG goals: If the organization has sustainability commitments, ensure AI initiatives are evaluated for carbon impact.
Evaluate geographic exposure: Where are your cloud workloads physically located? Regional energy markets influence cost and reliability.
Ask vendors about efficiency: Efficiency metrics matter. Responsible providers optimize power usage effectiveness (PUE) and integrate renewable energy.
Energy may be the lowest visible layer, but it shapes and supports the entire cake.
Layer 2: Chips and Computing — The Acceleration Engine
The second layer consists of semiconductors and processing units that enable AI. Graphics processing units (GPUs), tensor processors, and specialized AI chips accelerate the mathematical operations required for machine learning.
Companies such as NVIDIA have become synonymous with AI acceleration because modern AI workloads depend heavily on parallel processing capabilities. Similarly, cloud providers such as Advanced Micro Devices and Intel Corporation continue to compete in high-performance computing markets.
While most enterprises will not manufacture chips, understanding computing constraints is essential.
Why Chips and Compute Matter Strategically
Computing scarcity can limit innovation speed
Hardware supply chains influence AI timelines
Specialized processors affect model performance and cost
Vendor dependency creates strategic concentration risk
Compute is the bottleneck of modern AI. Limited availability or high demand can slow deployments or increase expenses.
Practical Planning Actions
Assess computing intensity before committing to AI use cases: Not all applications require large-scale model deployment.
Determine build vs. rent: Most organizations will rent compute through cloud providers, but some industries may justify hybrid approaches.
Understand workload classification: Training large models requires different compute than running them. Separate these planning exercises.
Diversify vendor exposure: Avoid single-point dependency in critical AI infrastructure.
The compute layer transforms raw electricity into usable intelligence capacity.
Layer 3: Cloud Data Centers — The Scalable Infrastructure
The third layer is where computing becomes accessible. Hyperscale cloud providers such as Amazon Web Services, Microsoft Azure, and Google Cloud operate global networks of data centers that house servers, storage systems, and AI accelerators.
Cloud infrastructure makes AI democratized and scalable. It removes the need for most enterprises to build physical data centers.
Why Cloud Matters Strategically
Enables elastic scaling
Reduces capital expenditure
Supports rapid experimentation
Integrates security and compliance tooling
Facilitates global deployment
However, the cloud also introduces governance complexity. Data sovereignty, cybersecurity exposure, and cost management are central leadership concerns.
Practical Planning Actions
Create a cloud AI governance model: Define who approves AI workloads, monitors usage, and controls cost.
Establish data classification policies: Sensitive data requires architectural segmentation.
Implement cost observability: AI workloads can escalate unexpectedly if not monitored.
Strengthen cybersecurity posture: AI expands attack surfaces. Security architecture must evolve with adoption.
Cloud is the delivery layer that connects computing power to business systems.
Layer 4: AI Models — The Intelligence Core
This layer is what most people mean when they say “AI.” Models are trained systems capable of pattern recognition, prediction, reasoning, or content generation.
Some organizations will use foundation models built by large providers. Others will fine-tune models for specific domain applications.
Models translate infrastructure into intelligence.
Why Models Matter Strategically
Model quality influences business outcomes
Bias and training data shape decisions
Licensing terms affect IP ownership
Explainability impacts regulatory compliance
The model layer is where ethics and governance become unavoidable. Models can scale good decisions, or they can amplify flawed assumptions.
Practical Planning Actions
Define acceptable risk levels: Not all business functions tolerate AI error equally.
Implement human-in-the-loop oversight: AI augments decision-making; it should not eliminate accountability.
Negotiate data usage terms: Understand how vendor models handle proprietary inputs.
Establish performance metrics: Measure accuracy, drift, fairness, and reliability.
Models are powerful, but they are not autonomous wisdom. Governance must travel with them.
Layer 5: Industry Applications — The Business Interface
The top layer is where AI touches customers, employees, and operations. Applications may include:
Predictive maintenance
Customer service automation
Financial forecasting
Supply chain optimization
Talent analytics
This layer is closest to revenue and value creation.
Why Applications Matter Strategically
They directly impact competitive positioning
They influence customer trust
They shape workforce adaptation
They determine ROI visibility
This is also where AI strategy becomes change management.
Practical Planning Actions
Prioritize high-impact, low-risk pilots: Build confidence before scaling.
Align AI initiatives with measurable KPIs: Tie projects to strategic objectives.
Prepare workforce training programs: Adoption depends on literacy and trust.
Communicate transparently: Customers and employees must understand how AI is used.
Applications are the visible frosting, but without the lower layers, they collapse.
How the Layers Interact
Each layer enables the one above it:
Energy powers computing
Computing drives cloud infrastructure
Cloud hosts models
Models enable applications
A single weakness in one layer constrains the rest. A shortage of computing capacity slows model training. Poor governance at the model layer undermines application trust. Rising energy costs influence cloud economics.
For business leaders, the lesson is clear: AI strategy is systemic.
Contracting vs. Building: A Layered Decision Framework
Most organizations will not build every layer themselves. Instead, they will selectively control some layers while outsourcing others.
A practical decision model might include:
Layer | Typical Ownership |
Energy | External dependency |
Chips & Compute | Mostly external |
Cloud | External with governance control |
Models | Mix of external and internal |
Applications | Primarily internal |
The deeper the layer, the more infrastructure-intensive it becomes. The higher the layer, the more competitive differentiation emerges.
For many enterprises, strategic advantage resides primarily in how applications are designed and governed, not in chip fabrication or energy generation.
Preparing for the Full Stack: Organizational Actions
Before launching AI initiatives, consider these enterprise-wide preparations:
1. Establish an AI Steering Council
Cross-functional oversight reduces fragmentation.
2. Develop an AI Risk Register
Map regulatory, operational, and reputational exposure.
3. Invest in Data Hygiene
Garbage in still means garbage out.
4. Redesign Talent Pathways
AI literacy must scale beyond IT departments.
5. Build Vendor Due Diligence Frameworks
Evaluate partners not just on performance, but resilience, ethics, and transparency.
Looking Ahead in the Series
This introductory blog establishes the structural view. The next five blogs will explore each layer in detail:
Blog 2: Energy and the Geopolitics of AI
Blog 3: Chips, Compute, and Capacity Constraints
Blog 4: Cloud Infrastructure and Governance
Blog 5: Models, Risk, and Competitive Differentiation
Blog 6: Applications and Organizational Change
Blog 7: Strategic Implications for the Future Enterprise
AI is not simply software. It is infrastructure, governance, economics, and culture combined.
Organizations that understand the layers will move deliberately. Those that do not may overinvest in frosting while neglecting the foundation.
The five-layer cake of AI is not just a metaphor. It is a strategic map. And leaders who study the map before baking will be better positioned to build resilient, ethical intelligence aligned with long-term value creation.
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