The $2 trillion AI industry isn’t one business — it’s a value chain with 7 layers, each with radically different economics. NVIDIA captures 80%+ margins at the bottom. AI application companies fight for 10% margins at the top. Understanding where you sit in this chain — and where value is migrating — is the most important strategic question in AI.
7 layers from user-facing to foundational — hover to explore each layer
▲ User-Facing
Layer 7
AI-Powered Services
Gross Margin
30-40%
Market
$200B+
McKinseyAccentureDeloitteBoutique AI Consultancies
Gross Margin35%
Human expertise augmented by AI. Highest labor costs, lowest scalability. AI is simultaneously the product they sell and the threat to their model.
Layer 6
AI Applications
Gross Margin
60-70%
Market
$100B+
CursorPerplexityMidjourneyHarveyJasperGleanNotion AI
Gross Margin65%
Where users interact with AI. Highest growth but lowest defensibility. Features that are novel today become table stakes tomorrow. Distribution and UX are the moat.
The orchestration layer. Connecting models to applications. RAG, fine-tuning, evaluation, deployment. Open source dominates but commercial wrappers are growing.
The intelligence layer. Training costs are enormous but falling. Model quality is commoditizing. Distribution and fine-tuning are becoming the real differentiators.
Layer 3
Data Infrastructure
Gross Margin
70-75%
Market
$90B+
SnowflakeDatabricksMongoDBConfluentPinecone
Gross Margin72%
Storing, processing, and serving the data that feeds AI. Vector databases are the new frontier. Data infrastructure becomes more critical as AI models need more structured input.
Layer 2
Cloud Compute
Gross Margin
60-65%
Market
$250B+
AWSAzureGoogle CloudOracleCoreWeaveLambda
Gross Margin63%
Renting compute at scale. Lock-in through data gravity and ecosystem integration. Capital-intensive but defensible. AI is the new growth driver for all hyperscalers.
Layer 1 — Foundation
Silicon & Chips
Gross Margin
75-80%
Market
$150B+
NVIDIAAMDIntelQualcommApple SiliconTSMC
Gross Margin80%
The foundational layer. GPU and custom silicon designed for AI training and inference. Highest margins in the entire chain. Hardware moats take decades to replicate.
▼ Foundational
The Economics of Each Layer
Margin tells you who captures value today. Defensibility tells you who keeps it tomorrow. Growth rate tells you where the market is heading. The layers with the best combination of all three are the most attractive competitive positions in tech.
Comparing All 7 Layers — 3 Key Metrics
Gross Margin
Silicon & Chips
80
Cloud Compute
63
Data Infra
72
Foundation Models
55
Middleware
70
Applications
65
Services
35
Defensibility
Silicon & Chips
95
Cloud Compute
85
Data Infra
75
Foundation Models
60
Middleware
55
Applications
40
Services
30
Growth Rate
Silicon & Chips
25
Cloud Compute
30
Data Infra
35
Foundation Models
50
Middleware
45
Applications
60
Services
20
Where Is Value Migrating?
The AI value chain is not static. Three structural forces are reshaping where value accrues — and they all point to the same conclusion: the middle of the stack is the most dangerous place to be.
3 Forces Reshaping the AI Stack
Trend 1
The Squeeze on the Middle
Squeezed
Foundation model providers are moving up into applications. Cloud providers are moving down into custom silicon. The middleware layer is getting squeezed from both directions.
Trend 2
Commoditization Cascades Upward
Commoditization rises
As each lower layer commoditizes, the layer above loses differentiation. Open-source models commoditize the foundation layer, which commoditizes AI applications built on them.
Trend 3
Distribution Beats Technology
Distribution > Tech
The companies capturing the most value aren’t always the most technically sophisticated. They’re the ones with the best distribution. OpenAI’s ChatGPT, Microsoft’s Copilot, Cursor’s developer adoption.
Where Should You Compete?
If you’re building an AI startup: pick a layer and go deep. Don’t try to vertically integrate until you dominate one layer. The companies that try to be a model provider AND an application AND a platform end up being mediocre at all three.
If you’re an incumbent: AI threatens your margin structure, not your existence. The question is which layer of the AI value chain maps to your existing moat. If you have distribution, you don’t need to build a model. If you have data, you don’t need to build an application — you need to make your data the most valuable input to other people’s models.
If you’re evaluating companies: follow the margins. High-margin layers with defensible positions compound value. Low-margin layers with commoditized offerings get disrupted. The best investments are in layers where margins are high AND defensibility is increasing — that’s Silicon and Data Infrastructure in 2026.
This analysis uses the VTDF framework to map competitive positions across the AI stack. The Exec Plan gives you the Master Skill — run full value chain analysis on any industry with 110 mental models.
663+ deep analyses. 110 mental models. 7 layers of AI decoded.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
