AI Value Chain

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. Andreessen Horowitz's 2023 analysis found infrastructure vendors capturing the majority of dollars flowing through the stack, while most model providers had not yet reached large commercial scale.

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.

Next steps

• Get a free workflow map →We map one of your workflows and give you a written verdict: software, a process change or AI. No obligation.
• AI consulting cost guide →Hourly rates and project budgets by firm tier, from independents to the Big Four.

Related terms

• AI Flywheel
  An 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 Split
  The 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 Strategy
  An 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.