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Three Requirements for Vertical Success: All Three, or None

You now have strategy (Snakes and Ladders), economics (super orchestrators), architecture (vertical intelligence), and a playbook (PPP). But execution requires three capabilities working in perfect sync. Lack any one, and you fail.

Requirement 1: Fine-Tuned Models with Domain Expertise​

Your subagents must be smarter than general-purpose AI. A general ChatGPT conversation does anything at 70% quality. Your finance subagent must do portfolio analysis at 99% quality—money is at stake. Your healthcare subagent must diagnose at 99% accuracy—lives are at stake.

This means fine-tuning the underlying model (Claude, Gemini, or others) on domain-specific data: financial earnings reports, healthcare clinical literature, education curriculum standards. The model learns the language, patterns, and nuances of your domain.

Without this, you're just a thin wrapper around general AI. Competitors replicate you in weeks.

Requirement 2: Deep Integrations with Existing Systems​

Your subagent must speak the language of incumbent systems. Not just read data from them, but write back in ways that respect workflows, security models, and approval processes.

A healthcare subagent that reads from Epic but can't write clinical notes in the right format is useless. A finance subagent that reads Bloomberg but can't execute trades through proper channels is a demo, not a product.

These integrations are expensive (months of API documentation, regulatory compliance, security audits) and they're defensible (competitors must rebuild them).

Without this, you're building in a sandbox, not serving real customers.

Requirement 3: Complete Agentic Solutions​

Your subagent must solve an end-to-end problem, not a slice of one. A healthcare subagent that reads clinical literature but doesn't integrate with hospital systems is a curiosity. A healthcare subagent that reads EHR, clinical literature, insurance rules, and FDA regulations, then recommends treatment plans doctors can act on immediately—that's a product.

This means coordinating five components (system prompt, horizontal skills, vertical skills, horizontal MCPs, vertical MCPs) in a workflow that makes sense to your customer.

Without this, you're a toy. With this, you're indispensable.

The OpenAI Lesson: Study Mode​

Consider OpenAI's Study Mode experiment (2024). OpenAI has:

  • Requirement 1: Models — Yes. GPT-4 is state-of-the-art
  • Requirement 2: Integrations — Partially. Study Mode integrates with some LMS platforms, but not deeply (Canvas, Google Classroom API, but not the full ecosystem)
  • Requirement 3: Agentic solution — Partially. Study Mode can answer questions, but it doesn't adapt learning paths, doesn't coordinate with teacher workflows, doesn't integrate grade books

Result: Study Mode is a feature, not a product. Teachers use it occasionally. It doesn't replace their workflow.

A PPP strategy would have:

  1. Built deep integrations with all major LMS platforms (Requirement 2)
  2. Fine-tuned a model on education data (Requirement 1)
  3. Built a complete solution: adaptive learning + teacher assistant + grading automation (Requirement 3)

With all three, Study Mode would be a $10M+ annual revenue business. Without all three, it's a feature OpenAI ships once and deprioritizes.

The Consequence of Missing Any Element​

If you have models + integrations but NO agentic solution, you're just a data pipeline. Useful, but not transformative.

If you have models + agentic solution but NO integrations, you're building in a sandbox. No real customer workflows.

If you have integrations + agentic solution but NO domain-specific models, you're a wrapper around general AI. Competitors replicate in weeks.

All three elements must work together. This is why PPP matters: it systematically builds all three. Phase 1 (infrastructure layering) addresses integrations. Phase 2 (market validation) provides domain expertise for model tuning. Phase 3 (strategic pivot) layers the agentic solutions.

Try With AI​

Use your AI companion tool set up (e.g., ChatGPT web, Claude Code, Gemini CLI), you may use that instead—the prompts are the same.

Prompt 1: Prioritize The Three Requirements​

The lesson says I need THREE requirements for success: (1) fine-tuned models, (2) deep integrations, (3) complete agentic solutions. Rank these from EASIEST to HARDEST for a beginner to achieve. For each one, explain: what would the first small step look like? Don't overwhelm me—just give me ONE concrete action per requirement.

Expected outcome: Prioritized roadmap: which requirement to tackle first (with first steps).

Prompt 2: Learn From Failure Cases​

The OpenAI Study Mode example shows what happens when you're missing requirements. Help me learn from this: Pick a different product or startup (maybe one I've heard of) that FAILED because it was missing one of the three requirements. Explain what they had, what they lacked, and what the lesson is for me.

Expected outcome: Pattern recognition from real failure case studies.

Prompt 3: Demystify Model Fine-Tuning​

I'm confused about 'fine-tuning models with domain expertise.' Explain this like I'm 10 years old. Then give me a realistic example: If I wanted to build a solution for [pick: teachers / small business owners / healthcare workers], what would 'fine-tuning' actually involve? Do I need to be a data scientist?

Expected outcome: Beginner-friendly understanding of model fine-tuning (without PhD-level jargon).

Prompt 4: Assess Incremental vs. All-Or-Nothing​

The lesson says 'all three or none'—meaning if I'm missing even ONE requirement, I fail. But that feels extreme. Help me understand: could I start with just ONE requirement (maybe integrations) and gradually add the others? Or is there really no middle ground? Give me an honest answer with reasoning.

Expected outcome: Honest assessment of whether you can build incrementally or need all three upfront.