By Sagar Shankaran, Founder of CallSphere
Understanding AI as a five-layer infrastructure stack — from energy generation to end-user applications — and why this framework matters for investment, strategy, and competitive positioning.
Key takeaways
When most people think about artificial intelligence, they think about chatbots, image generators, and coding assistants. These visible applications sit at the very top of a massive infrastructure stack that extends all the way down to energy generation, raw materials, and semiconductor physics.
Understanding AI as a multi-layered stack — analogous to how we think about the internet stack or the mobile ecosystem — is essential for anyone making strategic decisions about AI investment, deployment, or policy. Each layer has its own economics, bottlenecks, and competitive dynamics.
Every AI computation ultimately begins with electricity. Training a single frontier model can consume as much energy as a small city uses in a month. Inference at scale — serving billions of queries per day — requires continuous, reliable power at data center scale.
flowchart LR
CALLER(["Caller"])
subgraph TEL["Telephony"]
SIP["Twilio SIP and PSTN"]
end
subgraph BRAIN["Business AI Agent"]
STT["Streaming STT<br/>Deepgram or Whisper"]
NLU{"Intent and<br/>Entity Extraction"}
TOOLS["Tool Calls"]
TTS["Streaming TTS<br/>ElevenLabs or Rime"]
end
subgraph DATA["Live Data Plane"]
CRM[("CRM and Notes")]
CAL[("Calendar and<br/>Schedule")]
KB[("Knowledge Base<br/>and Policies")]
end
subgraph OUT["Outcomes"]
O1(["Booking captured"])
O2(["CRM record created"])
O3(["Human handoff"])
end
CALLER --> SIP --> STT --> NLU
NLU -->|Lookup| TOOLS
TOOLS <--> CRM
TOOLS <--> CAL
TOOLS <--> KB
NLU --> TTS --> SIP --> CALLER
NLU -->|Resolved| O1
NLU -->|Schedule| O2
NLU -->|Escalate| O3
style CALLER fill:#f1f5f9,stroke:#64748b,color:#0f172a
style NLU fill:#4f46e5,stroke:#4338ca,color:#fff
style O1 fill:#059669,stroke:#047857,color:#fff
style O2 fill:#0ea5e9,stroke:#0369a1,color:#fff
style O3 fill:#f59e0b,stroke:#d97706,color:#1f2937
This has created a new class of infrastructure challenge:
The energy layer is the ultimate constraint on AI scaling. No amount of algorithmic innovation can overcome a power shortage.
The semiconductor layer translates electrical energy into computational capability. This layer is dominated by specialized processors designed for the matrix multiplication and parallel computation that AI workloads demand.
Key dynamics at the chip layer:
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| Factor | Current State | Trend |
|---|---|---|
| Design leaders | A small number of companies dominate AI accelerator design | Increasing competition from startups and alternative architectures |
| Manufacturing | Concentrated in a handful of advanced fabrication facilities | Diversification efforts underway but years from impact |
| Supply constraints | Persistent shortages for cutting-edge chips | Easing as new fabs come online in 2026-2027 |
| Architecture innovation | GPUs dominate, but custom ASICs and neuromorphic chips are emerging | Workload-specific silicon will fragment the market |
The chip layer creates the most acute supply-demand imbalance in the AI stack. Access to advanced AI chips has become a geopolitical issue, with export controls, national stockpiling, and sovereign chip programs reshaping the landscape.
The infrastructure layer transforms chips into usable AI compute. This includes data centers, networking, storage, cooling systems, and the software that orchestrates distributed training and inference workloads.
A new concept has emerged at this layer: the AI factory. Unlike traditional data centers that serve diverse workloads, AI factories are purpose-built facilities optimized for AI training and inference. They feature:
The capital expenditure required for AI factories is staggering. A single large-scale training cluster can cost over a billion dollars. This has concentrated AI infrastructure among a small number of hyperscale cloud providers and well-funded AI labs.
The model layer is where raw compute becomes intelligence. Foundation models — large language models, vision models, multimodal models — are trained on massive datasets using the infrastructure described above.
This layer has its own sub-structure:
The economics of the model layer are evolving rapidly. While pre-training costs continue to rise for frontier models, the cost of inference and fine-tuning is falling precipitously. This creates an expanding market for organizations that consume AI capabilities without needing to train their own models.
The application layer is where AI meets users and businesses. This is the most visible and most diverse layer, encompassing:
The application layer captures the most value per unit of investment because it is closest to the end user. However, it is also the most competitive and the most dependent on the layers below it.
Understanding the AI stack helps identify where value accretes and where it leaks. Historically, the chip and infrastructure layers have captured outsized returns because they are constrained and capital-intensive. The application layer offers higher margins but faces intense competition and rapid commoditization.
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The stack framework clarifies build-versus-buy decisions. Most enterprises should operate primarily at the application layer, consuming model capabilities through APIs and cloud infrastructure. Only the largest organizations should consider investing in their own infrastructure or model training.
Each layer of the stack has different regulatory implications. Energy policy affects Layer 1. Export controls affect Layer 2. Data center regulations affect Layer 3. AI safety regulation affects Layer 4. Consumer protection law affects Layer 5. Effective AI policy requires understanding these distinctions.
One of the most important insights from the stack model is that bottlenecks migrate over time. In 2023-2024, the primary bottleneck was at the chip layer — demand for AI accelerators far exceeded supply. In 2025, the bottleneck shifted to the infrastructure layer as chip supply improved but data center construction lagged.
By late 2026, the bottleneck may shift again — this time to the energy layer, as the aggregate power demand of AI infrastructure begins to strain electrical grids in key regions.
Smart organizations anticipate where the bottleneck will move next and invest accordingly. The companies that secured power contracts and data center capacity two years ago are now reaping the benefits of foresight.
The AI stack continues to evolve. Edge computing, on-device inference, and federated learning are creating alternative paths that bypass the centralized infrastructure layers. Open-source models are reducing dependency on a small number of model providers. And new chip architectures may eventually break the current concentration at the silicon layer.
Understanding the stack as it exists today — while watching for structural shifts — is the foundation of any serious AI strategy.
The AI stack consists of five layers: energy generation at the base, semiconductor chips (Layer 2), compute infrastructure and data centers (Layer 3), AI models and algorithms (Layer 4), and end-user applications at the top (Layer 5). Each layer has distinct economics, competitive dynamics, and regulatory implications.
Understanding the AI stack helps organizations identify where bottlenecks and opportunities exist at each layer. Companies that anticipate where bottlenecks will migrate — from chips in 2023-2024 to infrastructure in 2025 to potentially energy by late 2026 — can invest ahead of constraints and gain competitive advantages.
Bottlenecks migrate through the stack over time. The primary constraint moved from chip supply (2023-2024) to data center capacity (2025), and is projected to shift to energy availability by late 2026 as aggregate AI power demand strains electrical grids. Organizations that secured power contracts and data center capacity early are now benefiting from that foresight.
Written by
Sagar Shankaran· Founder, CallSphere
Sagar Shankaran is the founder of CallSphere, where he builds production AI voice and chat agents deployed across healthcare, hospitality, real estate, and home services. He writes about agentic AI, LLM engineering, and shipping voice agents that handle real calls in production.
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