It has been rapidly evolving landscape around how applications interact with large language models (LLMs) and the implications for businesses, particularly regarding Managed/Model Computing Platforms (MCPs). We are observing these shifts is crucial for guiding our strategy and advising our clients.
The pace of change in the AI space is staggering. What seemed like a niche technical challenge just months ago is quickly being absorbed into the core offerings of major AI players.
TL;DR: Major AI providers like OpenAI and Anthropic (Claude) are increasingly integrating functionalities previously handled by separate MCPs (Managed Computing Platforms/Servers) directly into their services. This trend, signaled by OpenAI's Response API and Claude's built-in integrations, significantly lowers the barrier for users but raises strategic questions for businesses considering building custom MCP infrastructure. For most, especially smaller companies, focusing on leveraging these integrated platforms rather than building the underlying MCP bridge is likely the more prudent path forward.
Sensing the Tremors: Platform Integration is Accelerating
Earlier this year (around March), OpenAI's release of its Response API felt like an early signal. It suggested a move towards providing more built-in capabilities for managing interactions and perhaps state, reducing the burden on developers to handle everything externally.
Anthropic's recent Claude updates, incorporating numerous built-in integrations (reportedly around 10 MCP-like services) and allowing users to configure connections to their own MCP servers, strongly reinforces this direction. The message from these AI leaders seems clear: core interaction management and potentially basic customization/compute features are becoming part of the platform offering.
This is a natural evolution. For the average user or even many businesses, if the core AI service itself offers seamless, affordable integrations or managed functionalities, the appeal of using separate, potentially more complex tools (like standalone desktop clients requiring specific setups) or building custom infrastructure diminishes. Convenience and integration often win.
We also see infrastructure players like Cloudflare actively promoting solutions to ease the deployment of MCP servers, recognizing the demand. However, ease of deployment doesn't negate the strategic question of whether building one from scratch is the right move in the first place.
Why Small Companies Should Think Twice Before Building Custom MCP Servers
Based on these trends and the inherent dynamics of the AI market, building a dedicated, custom MCP server from the ground up presents significant challenges, especially for smaller organizations:
- High Cost & Complexity: Developing and maintaining robust, secure, scalable, and compliant infrastructure capable of efficiently handling AI model interactions is a non-trivial engineering and financial undertaking. This requires specialized expertise and ongoing investment.
- Rapid Pace of Change & Obsolescence: The underlying LLMs and their APIs are evolving at breakneck speed. A custom MCP built today might be outdated or incompatible with new platform features or models released just months later, requiring constant, costly adaptation.
- Commoditization of Core Functions: As OpenAI, Anthropic, Google, and others integrate more MCP-like functionalities directly, the unique value proposition of a basic, custom-built MCP erodes. Why build something yourself if the platform provider offers a similar, likely more integrated and potentially cheaper, solution?
- Resource Drain: For smaller companies, dedicating limited engineering talent, time, and capital to building foundational infrastructure like an MCP server means diverting those resources away from developing their core product, unique features, or go-to-market strategies where they might have a stronger competitive advantage.
- The Value Shift: The competitive differentiator is quickly moving away from being able to build the bridge (the MCP server) connecting users to AI. The real value now lies in how uniquely and effectively you use that bridge. It's about the specific application, the tailored workflow, the unique data integration, or the specialized user experience you build on top of the AI platforms.
Think of the short-video boom. Initially, just being able to shoot, edit, and post was novel. Soon, those basic capabilities became standard features within major platforms. The winners weren't necessarily those who built the best independent video editor, but those who created compelling content using the readily available tools. Similarly, the core ability to manage basic AI interactions is becoming table stakes provided by the platforms themselves.
Where Should Smaller Companies Focus Instead?
Given this landscape, a more strategic approach for most businesses, especially smaller ones, is likely to:
- Leverage Platform Capabilities: Fully utilize the built-in integrations, APIs (like OpenAI's Response API), and managed services offered by the core LLM providers.
- Focus on Application Layer Innovation: Build unique applications, specialized workflows, or vertical-specific solutions that use the AI platforms as a foundation. This is where true differentiation lies.
- Develop Smart Integrations: Connect AI capabilities intelligently into existing business processes and software using available APIs and tools.
- Partner for Expertise: Work with specialists (like Mercury Technology Solutions ) who understand both the AI platforms and business needs to design and implement effective, custom AI integrations without reinventing the wheel on core infrastructure.
Conclusion: Build Bridges Wisely
The AI world is moving incredibly fast. While the need for managing and customizing interactions with LLMs persists, the trend towards platform-integrated solutions is undeniable. Building a custom MCP server might seem appealing for control, but for many, particularly smaller companies, it risks becoming an expensive, rapidly outdated detour.
The smarter play is likely to focus resources on building unique value on top of the powerful, evolving platforms provided by the major AI players. Understand the tools, leverage their capabilities, and concentrate your efforts on solving specific customer problems in novel ways. That's where sustainable competitive advantage will be found in the age of AI.
