When we talk about the future of networking, AI is no longer just an add-on feature. It’s becoming the foundation. We are moving rapidly toward autonomous, AI-native networks that can self-heal, optimize in real-time, and handle massive data volumes with minimal human intervention.
But here is the hard truth: No single open source project can solve the AI-native networking puzzle on its own. Building an AI-ready network requires an entire pipeline of distinct, complex capabilities. It spans everything from raw data collection and underlying infrastructure to intelligent orchestration and intent-driven operations. Trying to cram all of this into a single monolithic project goes against the very cloud-native and disaggregated principles we’ve spent the last decade building.
Instead of looking for a silver bullet, we must look to a robust, interconnected open source ecosystem.
The Danger of the “Single Project” Trap
It is always tempting to hope for a single, all-encompassing project that solves every technical challenge. However, the transition from monolithic hardware/software appliances to a highly disaggregated model has taught us that specialized layers are necessary.
AI-native networks cannot exist in a vacuum. They require a delicate balance between fast proofs-of-concepts and production-grade stability that aligns with communication standards. A single project attempting to handle the infrastructure, the data pipeline, the AI models, and the network control loops would quickly collapse under its own weight and create fragmented silos.
The Linux Foundation Networking (LFN) serves as a critical collaboration hub where we don’t rely on a single tool. Instead, we foster an ecosystem where different projects contribute specialized, complementary building blocks.
The Building Blocks of an AI-Ready Network
To understand why an ecosystem approach is mandatory, we only need to look at the distinct layers required to make AI-native networking a reality:
1. The Cloud-Native Infrastructure Foundation
AI models are resource-intensive workloads. Before you can deploy intelligent automation, you need a reliable, cloud-native, and standardized telco footprint.
- Sylva & Anuket: Projects like Sylva and Anuket provide the essential infrastructure foundations and specifications. They ensure the underlying Telco cloud platform is unified, hardened, and ready to host intensive AI and network workloads without vendor lock-in.
2. Intent-Driven Operations & Management
AI thrives on declarative commands rather than rigid, manual scripting. We need systems that can take high-level business logic and translate it into actual network states.
- Nephio: By leveraging Kubernetes-native automation, Nephio manages complex network functions with the cloud-native agility and scalability that AI scaling demands.
- ONAP and Nephio: These modern management frameworks separate the declarative expression of intent from the imperative, under-the-hood implementation.
3. Network observability and Closed-Loop Automation
An AI model is only as good as the data feeding it. Autonomous networking requires real-time, accurate, and reliable telemetry data for decision-making.
- ONAP: From its inception, ONAP has focused on closed-loop automation and managing massive volumes of data generated by network functions. Its robust infrastructure for moving and storing data continues to evolve, providing the rich data pipelines that serve as the literal fuel for AI-driven network management.
- O-RAN SC (OSC): Provides the intelligent controller (RIC) architecture that is the foundation for creative AI Native Radio Access networks (AI-RAN)
4. Intelligent Agent development Optimization
AI-native capabilities must extend beyond the core network out to hyper-distributed edge and access deployments.
- Essedum: Enabling pushing the boundaries of intelligent Networks. It brings AI closer to the end-user for use cases such as optimizing traffic and radio resources where it matters most.
A Strong Feedback Loop with Standards
An ecosystem approach doesn’t just benefit software developers. It creates a vital feedback loop with Standard Definition Organizations (SDOs).
History has shown us that when open source communities work side-by-side with standards bodies, the entire industry wins. Open source projects can move quickly to deliver early implementations that inspire SDOs. Once those standards mature, the ecosystem rapidly integrates them back into production-ready software. We saw this beautifully when ONAP’s VES format was adopted by 3GPP for performance and fault monitoring. A true testament to the power of open collaboration.
The Road Ahead
The next decade of network evolution belongs to autonomous, intelligent systems. There are no more doubts regarding the role that open source plays in advancing communications.
But as we march toward this AI-native future, let’s not reinvent the wheel or lock ourselves into monolithic “all-in-one” platforms. The transition to AI-native networking is a team sport. If you want to build the future of telecom infrastructure, don’t look for a single project, but rather look to the ecosystem.
What are your thoughts on the ecosystem approach to AI in networking? How is your organization balancing the rapid evolution of open source with production stability? Let’s discuss in the comments.
Read more about how we are driving this community of practice: AI for Networks: A True Community of Practice
