The AI Company Landscape Map: 40+ Companies Across 6 Layers of the AI Stack
Each layer of the AI stack has distinct economics, moat structures, and value capture dynamics. Below: a VTDF breakdown of the key players at every level.
Key Components
Layer-by-Layer Analysis
Each layer of the AI stack has distinct economics, moat structures, and value capture dynamics. Below: a VTDF breakdown of the key players at every level.
Value Flows Down, Revenue Flows Up
The structural paradox of the AI industry: the layers that create the most durable value are not the layers that capture the most revenue today.
Run This Analysis on Any Company
This map uses the VTDF framework to assess each company's structural position across the AI stack .
Real-World Examples
AmazonMetaGoogleNvidiaOpenaiAnthropic
Key Insight
This map uses the VTDF framework to assess each company's structural position across the AI stack . The Business Engineer Exec Plan gives you the full engine: run VTDF analysis on any company with 110 mental models, plus the complete AI analysis archive.
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The AI Company Landscape Map
The AI industry isn’t one market — it’s a stack. From chips to foundation models to applications, every layer has different economics, different moats, and different winners. This map shows who builds what, where the value accrues, and which layers are most defensible.
Fastest growth. Distribution moats. Commoditization risk.
CursorAI-native coding. Developer love. Retention is exceptional.
JasperAI marketing copy. Early mover. Facing commoditization.
HarveyAI for law. Vertical moat. Enterprise contracts.
MidjourneyImage generation. Community + quality. No VC needed.
PerplexityAI-native search. Growing fast. Google is the risk.
GleanEnterprise search. Workplace AI. Data integration moat.
Salesforce EinsteinCRM-embedded AI. Distribution via install base.
value flows up ↑
Layer 6 — Services
AI-Enabled Services
Highest labor costs. Expertise moats. AI threatens margins.
McKinseyBrand moat. AI practice growing. Delivery model at risk.
Accenture AIScale + relationships. AI services arm. Core consulting at risk.
Deloitte AIAudit + advisory. AI augmentation. Regulatory moat.
Consulting FirmsExpertise bundling. Client lock-in. AI compresses margins.
AI AgenciesImplementation services. Low moat. Race to the bottom.
Layer-by-Layer Analysis
Each layer of the AI stack has distinct economics, moat structures, and value capture dynamics. Below: a VTDF breakdown of the key players at every level.
Layer 1 — Foundation
Compute & Chips
The bedrock of the AI stack. Every model, every inference, every AI product ultimately runs on silicon. This layer has the deepest moats and the highest margins — hardware advantages compound over years, not months. NVIDIA’s CUDA ecosystem is the defining lock-in of the AI era.
NVIDIA
Owns the training stack. CUDA lock-in. Data center GPUs are the new oil.
V
95
T
95
D
85
F
95
AMD
Credible alternative gaining share. ROCm catching up.
V
70
T
80
D
65
F
70
TSMC
Manufactures for everyone. Irreplaceable. Geopolitical risk.
V
85
T
95
D
75
F
85
Layer 2 — Infrastructure
Cloud & Infrastructure
The hyperscalers own the pipes. Every AI workload — training and inference — runs on their infrastructure. The lock-in is deep: data gravity, egress costs, and integrated AI services make switching painful. The battle is for who becomes the default AI cloud.
AWS
Default infrastructure. Deep lock-in. Bedrock expanding.
Best ML infrastructure. TPUs. Weaker enterprise distribution.
V
80
T
90
D
75
F
75
Layer 3 — Intelligence
Foundation Models
The most watched layer — and the most dangerous to invest in. Foundation models are commoditizing faster than anyone expected. The survivors will be those with distribution, not just capability. Open-source (Meta, Mistral) is compressing margins for everyone.
OpenAI
Consumer brand. GPT moat eroding. Distribution is the real advantage.
V
90
T
85
D
90
F
60
Anthropic
Safety positioning. Claude quality. Enterprise focus.
V
85
T
90
D
70
F
55
Google DeepMind
Deepest research bench. Gemini. Backed by Google’s resources.
V
80
T
95
D
75
F
80
Layer 4 — Enablement
Developer Tools & Platforms
The picks-and-shovels layer. These companies sell the tools that other companies use to build AI products. Community moats and developer adoption are the key dynamics — once a tool becomes the default, switching costs are high and growth compounds.
Hugging Face
Community moat. Open-source hub. GitHub of AI.
V
80
T
70
D
85
F
60
Databricks
Data lakehouse + AI. Enterprise data moat.
V
85
T
80
D
75
F
80
Cursor
AI-native coding. Developer love. Retention is exceptional.
V
85
T
75
D
70
F
65
Layer 5 — Application
AI Applications
The fastest-growing layer — and the most vulnerable. AI applications are where users experience AI directly. But without proprietary data or deep workflow integration, most applications are thin wrappers vulnerable to commoditization as models improve and competitors multiply.
Perplexity
AI-native search. Growing fast. Google is the risk.
The irony of the services layer: it captures the most revenue today, yet faces the deepest existential threat. Consulting firms sell expertise and implementation — exactly what AI is learning to automate. The smartest firms are racing to become AI platforms before AI replaces their delivery model.
McKinsey
Brand moat. Client relationships. AI threatens delivery model.
V
75
T
50
D
85
F
80
Accenture
Scale + relationships. AI services growing. Core consulting at risk.
V
70
T
55
D
80
F
75
Value Flows Down, Revenue Flows Up
The structural paradox of the AI industry: the layers that create the most durable value are not the layers that capture the most revenue today. But the AI era is inverting this — value AND revenue are shifting down toward infrastructure — as explored in the economics of AI compute infrastructure — and models.
The AI Value Paradox
Def.
Rev.
Defensibility
Value creation flows DOWN
AI-Enabled Services
40
5%
AI Applications
50
15%
Developer Tools & Platforms
70
10%
Foundation Models
60
15%
Cloud & Infrastructure
85
25%
Compute & Chips
95
30%
Revenue Today
Revenue historically flows UP
The AI era is inverting the stack. Historically, services and applications captured most enterprise spending. But AI is shifting both value and revenue downward — toward infrastructure and compute. The companies closest to silicon are building the deepest moats and the fattest margins.
The Great Inversion
Run This Analysis on Any Company
This map uses the VTDF framework to assess each company’s structural position across the AI stack. The Business Engineer Exec Plan gives you the full engine: run VTDF analysis on any company with 110 mental models, plus the complete AI analysis archive.
The Business Engineer Exec Plan
Map Any Company Across the AI Stack
The VTDF framework, 110 mental models, the full AI analysis archive, and Claude OS — the analytical engine behind this landscape map. Built for executives, investors, and strategists who need structural clarity.
What is The AI Company Landscape Map: 40+ Companies Across 6 Layers of the AI Stack?
Each layer of the AI stack has distinct economics, moat structures, and value capture dynamics. Below: a VTDF breakdown of the key players at every level.
What is Layer-by-Layer Analysis?
Each layer of the AI stack has distinct economics, moat structures, and value capture dynamics. Below: a VTDF breakdown of the key players at every level.
What is Value Flows Down, Revenue Flows Up?
The structural paradox of the AI industry: the layers that create the most durable value are not the layers that capture the most revenue today. But the AI era is inverting this — value AND revenue are shifting down toward infrastructure and models.
What is Run This Analysis on Any Company?
This map uses the VTDF framework to assess each company's structural position across the AI stack . The Business Engineer Exec Plan gives you the full engine: run VTDF analysis on any company with 110 mental models, plus the complete AI analysis archive.
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.
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