Why Depth, Not Scale, Will Define the Next Generation of Ai Winners

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Every AI cycle follows a familiar curve: technological breakthroughs ignite optimism about universal solutions, followed by the realization that industries do not adopt general intelligence—they adopt contextual systems.

In 2025, we’ve entered the industrialization phase of AI: the shift from capability to deployment, from large model demos to domain-specific operations. The future belongs to those who go deep, not wide.

This entire newsletter was kind of inspired this tweet by Box CEO Aaron Levie

1. From Models to Markets: The Real Bottleneck

Foundation models—GPT-4o, Claude 3, Gemini 2—have reduced the marginal cost of intelligence. But they haven’t reduced the friction of implementation.

Key data points:

• 85% of enterprise AI pilots fail to reach production (Gartner, 2024).

• The median enterprise takes 9–12 months to move from prototype to production, mostly due to compliance, integration, and data access.

• Enterprises now spend 3–5x more on “last-mile AI” (customization, security, and workflow tuning) than on inference costs.

2. The Economics of Depth

Horizontal AI companies chase TAM through abstraction; vertical AI companies create defensibility through context.

Result:
While horizontal AI platforms face pricing compression (OpenAI API prices down ~70% YoY), vertical AI startups with workflow integration maintain 50–70% gross margins and multi-year contracts.

3. Four Moats of Vertical AI

Defensibility is shifting from model IP to operational embedding. The next generation of category leaders will be built around these interlocking moats:

🧠 Proprietary Data

• Real advantage comes from closed data: radiology images, supply chain telemetry, defense sensor networks.

• Proprietary data pipelines compound faster than model architectures—because they improve model relevance, not just accuracy.

⚙️ Workflow Integration

• 70% of enterprise software budgets go to process automation and data integration.

• Deeply embedded AI systems turn from “apps” into infrastructure.

• Switching costs grow exponentially once a model touches live decision systems.

🛡️ Governance & Trust

• SOC 2, HIPAA, ISO 27001 aren’t checkboxes—they’re barriers to entry.

• 80% of AI deals over $1M/year include explicit governance or auditability requirements.

👥 Human Lock-In

• Trust compounds socially. Once a product becomes part of institutional memory, replacement costs include retraining, compliance recertification, and cultural adaptation.

• Palantir’s average contract duration: 6.5 years. That’s human lock-in at scale.

4. Why Foundation Models Will Stay Horizontal

The hyperscalers—OpenAI, Anthropic, Google DeepMind—will continue optimizing for scale:

• They train trillion-parameter systems for general intelligence.

• They reduce inference costs (Claude 3.5 API pricing ↓ 60% YoY).

• They monetize through volume, not specialization.

But they cannot do the following at scale:

• Negotiate data access with 500 hospitals.

• Certify models under FAA, FDA, or FINRA regimes.

• Rebuild domain-specific workflows for every regulated sector.

Their business model (scale) is the mirror opposite of the vertical AI model (depth).

5. The Vertical Opportunity: Trillions in Untransformed Value

When measured not by software spend but by work value, vertical industries represent trillions in untapped AI opportunity:

Observation: These are not “niche” markets—they are civilization’s operating system.

Depth creates defensibility because it aligns with complexity.

6. Depth as the New Scale

Defensibility is no longer about who trains the biggest model. It’s about who builds the deepest stack around real-world systems: data, workflow, governance, and human trust.

• The Vertical AI company of the next decade will look less like a startup and more like a system integrator with embedded intelligence.

• The new “platforms” will emerge bottom-up—sector by sector—until they collectively redefine the enterprise stack.

7. The Next Frontier

The next wave of category leaders will:

• Build specialized agents around domain-specific ontologies.

• Leverage foundation models as infrastructure, not differentiation.

• Capture data gravity in trillion-dollar markets that can’t be generalized.

The winners won’t outscale OpenAI.
They’ll out-understand their customers.

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