📖 AI-Native Infrastructure: Architecture evolution guide from cloud-native to AI-native

AI-Native Infrastructure

Through writing, publishing, and engineering practice, systematically build engineering methodologies for AI-native infrastructure.

What Is AI-Native Infrastructure?

Infrastructure redesigned for AI systems — not retrofitted from cloud-native stacks.

Agent / AI Applications
Agentic Runtime & Context
Inference · Training · Governance
GPU & Accelerated Infrastructure
  • NON-DETERMINISM AI workloads are non-deterministic by nature
  • AGENT-FIRST Agents, not services, are primary execution unit
  • FIRST-CLASS RESOURCES GPU, context, and tokens become first-class resources
  • GOVERNANCE > DEPLOY Scheduling and governance matter more than deployment

Core Technology Domains

These directions constitute my ongoing research focus, emphasizing practical engineering abstraction and implementation boundaries.

AI Native Infrastructure

AI Native Infrastructure

I focus on GPU virtualization, inference, and agent runtime and engineering abstractions, thinking about how to deliver capabilities to production environments stably and efficiently.

Cloud Native

Cloud Native

I study Kubernetes’s boundaries and evolution under AI workloads, including resource scheduling, elastic scaling, and multi-tenant governance.

Open Source

Open Source

I participate in and promote the AI-Native Infrastructure open source ecosystem from an engineer’s perspective, valuing verifiable, evolvable designs and collaboration methods.

About Jimmy Song

Jimmy focuses on AI-Native Infrastructure and cloud native application architecture, researching engineering problems such as GPU virtualization, heterogeneous computing scheduling, and system governance. Jimmy currently serves as Open Source Ecosystem VP at Dynamia.ai, and is also a CNCF Ambassador and founder of the Cloud Native Community (China).

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