AI + MCP Protocol: The New Infrastructure Layer for Agentic Businesses
Bottom Line: Model Control Protocol (MCP) represents a potential “HTTP moment” for AI, creating universal connectivity between LLMs, tools, and data sources. This could commoditize many current AI service businesses while creating massive opportunities for infrastructure and aggregation layers.
What is Model Control Protocol (MCP)?
MCP serves as a standardized connector layer that sits between:
- LLM applications (Claude, ChatGPT, custom AI agents)
- Data sources (databases, APIs, file systems)
- Tools and services (productivity apps, analysis tools, automation platforms)
Think of MCP as creating universal “pipes” that allow any AI agent to connect to any data source or tool, regardless of the underlying technology stack.
The Disruption Thesis
Current AI Service Business Model
Many entrepreneurs have built $10k ARR AI service businesses with this structure:
- Custom AI agents for specific industries
- Static RAG (Retrieval Augmented Generation) systems
- Prompt flows tailored to customer requirements
- Manual integrations with multiple tools and data sources
Example: “AI-powered SEO service” combining:
- Multiple LLMs for content generation
- Industry-specific RAG databases
- Social analytics integration
- Content scheduling and customization
How MCP Changes Everything
Before MCP: Each AI service requires custom integrations
- Separate connectors for each data source
- Custom prompt engineering for each tool
- Manual workflow orchestration
- High development and maintenance costs
After MCP: Universal connectivity layer
- Single MCP server handles all integrations
- Standardized data access across tools
- Automated workflow orchestration
- Dramatically reduced development time
Result: What previously required months of custom development can now be assembled in weeks using MCP-powered components.
Investment Opportunities in the MCP Era
1. Industry-Specific MCP Aggregation Layers
Opportunity: Build “MCP servers” that aggregate tools, data sources, and agents for specific industries.
Example - AI-Driven Sales Infrastructure:
- MCP server combining CRM data, email tools, social media, and analytics
- Industry-customized AI agents (SaaS sales, real estate, financial services)
- Plug-and-play setup for sales teams
- Subscription revenue model at $500-2000/month vs current $10k custom builds
Key Advantage: First-mover advantage in creating industry-standard MCP implementations
2. AI Agent Marketplaces & Orchestration
Vision: “App Store for AI Agents” powered by MCP connectivity
- Pre-built agents for common business functions
- Easy integration with existing business tools
- No-code/low-code agent customization
- Revenue sharing with agent developers
Market Size: Current AI services market ~$50B, growing 25%+ annually
3. Data Infrastructure & Security Layers
Critical Need: As MCP makes data access easier, security becomes paramount
- Data governance for MCP connections
- Privacy-preserving agent interactions
- Audit trails for AI decision-making
- Compliance frameworks for regulated industries
Sector Analysis: Where MCP Creates Most Value
High-Impact Sectors
1. Professional Services
- Legal document analysis + case research
- Financial modeling + market research
- Consulting report generation + industry data
2. Sales & Marketing
- Lead generation + CRM automation
- Content creation + social media management
- Customer analysis + personalization
3. Operations & Supply Chain
- Inventory management + demand forecasting
- Quality control + process optimization
- Vendor management + procurement
Lower Impact (For Now)
1. Highly Regulated Industries
- Healthcare (data privacy concerns)
- Financial services (compliance requirements)
- Government (security clearance needs)
2. Creative Industries
- Brand strategy (requires human judgment)
- Creative direction (subjective decision-making)
- Client relationship management (personal touch needed)
Investment Framework for MCP Opportunities
Key Questions for Evaluation
Market Timing:
- Is this industry ready for AI automation?
- Are current manual processes expensive and repeatable?
- Will customers pay for AI-powered solutions?
Technical Moat:
- Can you build defensible MCP integrations?
- Is there proprietary data or algorithm advantage?
- How quickly can competitors replicate the solution?
Business Model Durability:
- Does this create ongoing value or one-time efficiency?
- Can you build network effects through data or community?
- Is pricing sustainable as MCP tools commoditize?
Red Flags to Avoid
❌ Pure aggregation plays without unique value-add
❌ Industries with strong incumbent software (hard to displace)
❌ Complex regulatory requirements (slow adoption)
❌ Low willingness to pay for AI solutions
Green Flags for Investment
✅ Clear ROI demonstration (time savings, cost reduction)
✅ Network effects potential (data improves with usage)
✅ Expanding use cases within same customer base
✅ Strong founder-market fit with industry expertise
Personal Experience: Testing MCP Applications
Current Experimentation: I’ve been testing MCP implementations in Windsurf and other development environments. The early results are promising - complex integrations that previously required days of custom coding can now be accomplished in hours.
Use Cases Tested:
- Financial data analysis across multiple sources
- Investment research automation
- Content generation with real-time market data
- Due diligence workflow automation
Key Insight: The bottleneck shifts from technical integration to workflow design and business logic optimization.
Timeline & Adoption Predictions
Near-term (6-12 months)
- MCP adoption in developer tools and productivity apps
- Early AI service businesses begin using MCP for faster deployment
- First industry-specific MCP aggregation platforms launch
Medium-term (1-3 years)
- Enterprise adoption of MCP-powered AI workflows
- Consolidation of point-solution AI service businesses
- Major cloud providers (AWS, Google, Microsoft) integrate MCP standards
Long-term (3-5 years)
- MCP becomes standard for AI-business system integration
- New category of “AI infrastructure companies” emerges
- Significant job displacement in routine knowledge work
Action Items for Entrepreneurs and Investors
For AI Entrepreneurs
- Evaluate current business model resilience to MCP disruption
- Identify aggregation opportunities in your industry expertise area
- Experiment with MCP implementations to understand capabilities
- Build relationships with potential enterprise customers early
For Investors
- Due diligence on AI portfolio companies - how MCP-resistant are their moats?
- Scout for MCP-native startups building industry-specific solutions
- Consider infrastructure investments in MCP tooling and security
- Monitor adoption metrics in target sectors
Conclusion
MCP represents a fundamental shift in how AI systems interact with business infrastructure. While this may commoditize many current AI service businesses, it creates enormous opportunities for companies that can build defensible aggregation layers and industry-specific solutions.
For Investors: Focus on companies building network effects and proprietary data advantages rather than pure technical integrations.
For Entrepreneurs: The question isn’t whether MCP will disrupt your current model, but how you can leverage it to build something bigger and more defensible.
What’s your automation use case? If you’re experimenting with AI workflows or building MCP-powered solutions, I’d love to hear about your experiences and challenges.
Views based on hands-on experimentation with MCP implementations and analysis of 50+ AI service business models. Technology adoption timelines remain speculative - actual pace may vary significantly.
