What Is AI Native and Why Is MCP Key?

Published: Wednesday, December 10, 2025 at 4:00 pm

AI Native: A New Era in Computing

The term "AI native" is gaining traction, but its meaning remains undefined, unlike its predecessor, "cloud native." While "cloud native" emerged organically to describe a significant shift in system architectures, "AI native" is currently more of an aspirational marketing term. The industry is still in its early stages of the Autonomous Era, with many technologies, practices, and patterns still emerging or being misunderstood.

The rise of cloud native applications provides a valuable roadmap for the future. The evolution of Model Context Protocol (MCP), a technology that is barely a year old, offers insights into the development of AI native. MCP's rapid adoption and iterative improvements, driven by real-world needs, exemplify the learning process.

AI is transforming the entire IT stack, with a massive architectural shift underway. At the infrastructure layer, niche technologies are now being broadly deployed for new AI factories. Horizontal scaling is shifting towards a right-scaling model, and accelerated computing is moving from CPUs to specialized processors. AI agents, which deliver significant value, rely on generative AI and machine learning inference engines. These systems can handle uncertainty and edge cases, but they still require human oversight.

The industry is in the process of understanding how AI will transform the IT stack, and the adoption of AI agents is where the real learning will happen. The emergence of MCP and its rapid evolution is an example of this learning process. The open-source Agentic AI Foundation will accelerate innovation and create an MCP ecosystem, driving the industry toward AI native.

BNN's Perspective: The evolution of AI native is a complex process, mirroring the development of cloud native. While the term "AI native" is still being defined, the rapid advancements in technologies like MCP and the open-source initiatives are promising signs of progress. The industry must embrace iterative development and learning to fully realize the potential of AI.

Keywords: AI native, cloud native, Model Context Protocol, MCP, Autonomous Era, AI agents, generative AI, machine learning, infrastructure, architectural shift, open source, Agentic AI Foundation, Anthropic, Kubernetes, cloud computing.

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