A live webinar on the Supply Chain of Intelligence™ framework, AI compression, rising dependency economics, and how founders should reposition before their layer gets absorbed.

---

Who This Is For

Founders, CEOs, CTOs, product leaders, and AI teams building:

• SaaS products
• AI-native applications
• copilots
• workflow automation platforms
• enterprise AI tools
• agentic systems

---

The Core Problem

Most AI companies are competing on features while the real battle is happening deeper in the stack.

As AI juggernauts expand upward:

• models absorb workflows
• platforms bundle capabilities
• surfaces commoditize
• margins compress
• dependency costs rise across the ecosystem

Many founders still cannot clearly answer:

• Where does our product actually sit in the AI stack?
• Which layers are becoming commodities?
• Where does durable value accrue?
• What happens if the layers beneath us absorb our functionality?
• What compounds over time in our product?
• Are we building defensibility — or renting intelligence?

Most products sit higher in the stack than they realize.

This webinar introduces a structural framework for understanding where AI value is created, where products become vulnerable, and how companies can reposition before compression accelerates.

---

What You’ll Learn

The Supply Chain of Intelligence™

A practical framework for understanding how modern AI products are structured across:

• infrastructure
• models
• execution
• orchestration
• surfaces
• trust systems
• and institutional memory

You’ll learn:

• where your company sits
• which layers are exposed
• which layers compound
• where durable moats still exist

---

The AI Compression Wave

Why:

• model providers
• hyperscalers
• platform ecosystems
• AI-native incumbents

are increasingly compressing companies above them in the stack.

We’ll explore:

• how compression works
• why some products become features
• why others compound
• how value migrates across layers over time

---

The Hidden Economics of AI Dependency

AI costs are no longer just about tokens.

Founders are increasingly exposed to:

• usage-based pricing shifts
• orchestration overhead
• inference dependency
• retrieval costs
• memory persistence
• verification complexity
• upstream pricing pressure

Every layer beneath you in the stack increasingly influences the economics of the layer above you.

We’ll discuss:

• who gets squeezed hardest
• why
• what structural moves reduce long-term exposure

---

Where Defensibility Still Exists

Not all layers are equally vulnerable.

We’ll explore where durable value is still accumulating:

• domain execution
• workflow ownership
• orchestration
• verification systems
• institutional memory
• embedded organizational intelligence

---

Live Company Mapping (If time permits)

We’ll also analyze real companies through the framework, including examples like:

• OpenAI
• Microsoft
• Gamma
• Cursor
• Harvey

and examine:

• where they sit
• where they’re expanding
• what they own
• where compression risk exists

---

What You’ll Leave With

→ A clear understanding of where your product sits in the AI stack

→ A structural lens for evaluating defensibility and compression risk

→ A better understanding of AI dependency economics and pricing pressure

→ A roadmap for where your company should move next to become harder to replace

→ A shared vocabulary for discussing AI strategy internally with product, engineering, and leadership teams