
Overview
The AI economy is consolidating into a three-layer stack where value capture follows a predictable gradient: interface dominates, intelligence competes, infrastructure commoditizes.
Each layer feeds the one above it — data powers reasoning, reasoning powers attention — but economic gravity flows upward.
In this architecture, whoever owns the user relationship owns the margin.
Layer 1: Interface & Attention Capture
Role: User Relationship Owner
Platforms: ChatGPT, Claude, Gemini, Perplexity, vertical AI assistants, and workflow-native copilots.
This is where trust, habit, and loyalty are formed — the digital equivalent of the browser in Web 1.0 or the social feed in Web 2.0.
Here, AI becomes the operating layer of cognition.
Mechanism of Value
- Attention → Loyalty → Monetization
- Direct user subscription (e.g., $20–200/month)
- Enterprise copilots and embedded AI apps
- API premium access and upselling to reasoning engines
- Contextual integrations within productivity or creative workflows
Why It Wins
- Control of Context: Interfaces determine what users see, trust, and pay for.
- Behavioral Lock-In: Reinforced by personalized memory and continuous dialogue loops.
- Ecosystem Gravity: Third-party tools orbit around these interfaces via SDKs and app stores.
Economics: High Margin
- Subscription + enterprise + upsell model
- Direct path from engagement to cash
- 65–80% total value capture
Core insight: The interface doesn’t just control the conversation — it controls the economics.
Layer 2: Orchestration & Reasoning
Role: Intelligence Layer
Players: OpenAI, Anthropic, Google DeepMind, Mistral, Cohere, and others competing on reasoning speed, cost, and context fidelity.
This layer provides cognition-as-a-service — the logic that interprets intent, retrieves data, and generates insight.
Mechanism of Value
- Per-token or per-query pricing models
- Differentiation through reasoning depth, latency, multimodal comprehension, and reliability
- Integration into enterprise workflows, agent frameworks, and developer ecosystems
Competitive Dynamics
- Commoditization Pressure: Every LLM competes on marginal cost per token.
- Defensibility Shifts to Ecosystem: SDKs, agent frameworks, and developer loyalty matter more than pure model performance.
- Dual Strategy: Build for both horizontal scale (API dominance) and vertical specialization (domain copilots).
Economics: Moderate Margin
- Margins erode as cost per token declines
- Price elasticity limits upside; switching costs are low
- 15–25% total value capture
Core insight: Intelligence alone doesn’t capture value — orchestration and developer integration do.
Layer 3: Data & Infrastructure
Role: Commodity Plumbing
Providers: Google and Bing (search APIs), large databases, data brokers, cloud infrastructure providers, and open data networks.
This is the substrate everything else depends on — search indices, crawlers, and content repositories that feed higher layers.
It’s the foundation of retrieval, but no longer the frontier of value.
Mechanism of Value
- Utility-based API pricing (per query or data call)
- Licensing for specialized datasets or real-time feeds
- Infrastructure reliability as competitive differentiator (latency, uptime, freshness)
Why It’s a Low-Margin Layer
- High fixed cost, low differentiation
- API commoditization drives down per-call pricing
- LLM caching and internal retrieval reduce dependency over time
Economics: Low Margin
- 5–10% total value capture
- Margins resemble cloud or bandwidth economics
- Providers become utilities for the AI economy
Core insight: Data remains essential — but only as a low-margin input.
The Flow of Intelligence and Value
| Flow Direction | Function | Value Gradient |
|---|---|---|
| Bottom → Top | Data feeds intelligence, intelligence powers interfaces | Utility → Insight → Experience |
| Top → Bottom | Attention monetizes reasoning, which funds infrastructure | Attention → Capital → Scale |
Intelligence scales from below, but value concentrates above.
Whoever controls the attention surface controls the cash flow of the entire stack.
Strategic Implications
For Interface Players (Top Layer):
- Build habit loops through personalization and memory.
- Expand vertically with plugins, copilots, and agent ecosystems.
- Capture pricing power through direct enterprise contracts.
- Treat reasoning providers as interchangeable backends.
For Reasoning Providers (Middle Layer):
- Differentiate via orchestration (agents, frameworks, SDKs).
- Develop proprietary reasoning modes or proprietary data pipelines.
- Form exclusive partnerships with interface leaders to lock in usage.
For Infrastructure Providers (Bottom Layer):
- Focus on data freshness, access control, and licensing.
- Monetize via volume and reliability, not uniqueness.
- Pursue co-ownership or strategic partnerships with reasoning layer providers to avoid disintermediation.
The Structural Law of the AI Economy
As intelligence becomes abundant, attention becomes scarce.
As cognition commoditizes, relationship capital becomes the moat.
- Interface = Ownership
- Intelligence = Competition
- Infrastructure = Dependence
The AI stack will not be defined by who has the smartest model,
but by who owns the loop between cognition and loyalty.









