AI Value Chain
The AI value chain describes the layered set of activities — from chip and energy supply through foundation-model training to applications — that produces deployed AI value, and where economic margin accumulates within that stack.
Full definition
A common five-layer view: (1) Compute & energy (NVIDIA, TSMC, hyperscaler data centers, utilities); (2) Cloud & infrastructure (AWS, Azure, GCP, Oracle); (3) Foundation models (OpenAI, Anthropic, Google, Meta, Mistral); (4) Tooling & orchestration (LangChain, LlamaIndex, vector DBs, observability); (5) Applications & vertical AI. As of 2025, margins are concentrated at layers 1 and 3, while layer 5 is fragmented and competitive — the same pattern as cloud vs SaaS in the 2010s.
Why it matters
Strategy depends on where in the chain a company operates. Application-layer companies that build defensible workflows, data, and distribution can still earn outsized returns; thin GPT wrappers cannot. The chain view also informs make-vs-buy and partner decisions.
Example
An insurer sells an AI claims-triage product. It does not train models (layer 3) but owns proprietary claims data, regulatory know-how, and customer relationships (layer 5) — building defensibility on top of commodity foundation models.
Related terms
- AI FlywheelAn AI flywheel is the self-reinforcing loop in which product usage generates proprietary data, that data improves the underlying models, better models attract more usage, and the gap to competitors widens over time.
- Build-Train-Deploy SplitThe build-train-deploy split is the allocation of AI investment across three phases — building infrastructure and data pipelines, training or fine-tuning models, and deploying and operating them — and how that allocation shifts as a portfolio matures.
- AI StrategyAn AI strategy is a written, board-level plan for how an organization will use AI to create competitive advantage — naming the business goals, prioritized use cases, required capabilities, governance posture, partner choices, and a 12-36 month investment plan.
- AI-Native (Firm / Product)AI-native describes a firm or product designed from inception around AI as the core production function — where AI is not a feature on top of legacy systems but the substrate that data flow, decisions, and value creation are built on.
Source & further reading
Primary source: McKinsey & Company — "The state of AI in 2024" (2024).
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