Originally published as sponsored content produced by Axios Creative House and reposted here with permission, this article examines why network infrastructure for AI is critical to enterprise success. At the center of the conversation is the role programmable networking, scalability and near real-time data movement play in helping enterprises compete and win in the AI economy. Read the original post.
The next phase of AI will be defined by execution
As organizations work to deploy AI at scale, they must balance performance, cost and flexibility across increasingly dispersed environments.
Lumen President and CFO Chris Stansbury discusses how network infrastructure is now central to the AI economy and what it will take to support long-term enterprise adoption.
Why is the network a critical factor in determining whether AI investments deliver value?
As enterprises race to invest in the models, chips and data needed for AI, Lumen argues that the real constraint is the network itself.
Stansbury: AI has made something very clear: Compute can be added, but data must move from point A to point B, fast and securely, all the time.
As AI workloads have become dispersed across clouds, data centers and the edge, what matters isn’t just having access to more GPUs, it’s whether you can move massive volumes of data in real time, reliably and cost effectively.
If the network can’t keep up, the compute you’ve invested in sits underutilized, and the economics of AI break down.
What does it take to build the backbone for the AI future?
Lumen made this bet to invest in the physical infrastructure, programmable network and connected ecosystem to support enterprises’ AI ambitions.
Stansbury: Operationally and financially, it’s a multi-year effort to build a platform that can handle how AI works: highly distributed, data intensive and always on.
That means expanding high-capacity fiber and interconnection at scale, while simultaneously building the software layer that makes the network programmable so you can dynamically route traffic, prioritize workloads and move data anywhere it needs to be, in real time.
Culturally, that’s the bigger shift. We’ve moved from defense to offense. Lumen is playing to win. And the enterprises that will win in the AI economy no longer treat the network as static infrastructure; they view it as a strategic asset.
What makes Lumen uniquely positioned to build this kind of AI backbone at scale?
Stansbury: What differentiates Lumen is that we’ve invested across all the layers required to make AI work. We have an expansive physical network footprint, a rapidly scaling programmable layer and a growing ecosystem of cloud and technology partners.
Building the backbone for AI requires sustained multi-year capital, a software-led architecture and the discipline to build ahead of demand.
How do physical network assets and the software-defined layer work together to change what enterprises can do with AI?
You've described programmable networking as the control plane for Cloud 2.0.
Stansbury: The physical network gives you the foundation: high-bandwidth, low-latency connectivity at scale. The programmable layer adds intelligence and real-time control.
When you bring those together, the network stops being passive transport and becomes an active control layer for distributed systems. It’s what allows enterprises to operate AI workloads across clouds, data centers and the edge with required speed and precision.
What do recent gains say about how enterprises are rethinking AI and cloud workloads?
Lumen doubled its NaaS customer base in less than a year and has signed $13 billion in Private Connectivity Fabric (PCF) deals.
Stansbury: Enterprises are realizing AI is fundamentally a data movement problem.
They need dedicated capacity to move massive volumes of data, which is driving long-term PCF commitments, and they need flexibility, which is accelerating adoption of on-demand, consumption-based networking (Network-as-a-Service).
The combination reflects a broader shift toward AI-native architectures where performance and control of the network directly impact outcomes.
Why is this as much an economic story as a technology story?
You’ve said this isn’t just about speed—it’s also about economics.
Stansbury: At scale, AI is as much about economics as it is about performance. These workloads are incredibly data-intensive, which means the cost of moving and managing that data becomes a defining factor in ROI.
A programmable, consumption-based model changes that. It allows enterprises to align cost with usage, move workloads to the most efficient environments and avoid the friction of rigid architectures.
So, the network becomes an economic control point, not just a technical one.
Why does programmability matter so much for AI-first organizations?
Stansbury: Programmability matters because AI workloads are inherently dynamic.
It gives enterprises the ability to respond in real time, by scaling capacity, rerouting traffic and optimizing data flows as conditions change, without relying on manual processes.
This moves the network much closer to a cloud operating model: on-demand, policy-driven and adaptable.
If this conversation is revisited in 2028, what will success look like?
Stansbury: Success is a network that operates as a fully integrated, programmable platform that is consumed on demand and spans clouds, data centers and edge environments.
Ultimately, success is when the network accelerates AI adoption by removing friction instead of introducing it.
What separates companies that will win in the AI era from those that won't?
Stansbury: The companies that win will:
- Treat the network as a strategic lever.
- Design for real-time data movement across distributed environments.
- Invest in programmability.
- Build architectures that can adapt as workloads evolve.
The ones that fall behind will continue operating on static, fragmented networks that weren’t designed for the demands of AI.
In an AI-driven world, how effectively you can move data is paramount. And that’s increasingly what determines speed, cost and competitive advantage.
AI success depends on moving data when and where it's needed. Explore how on-demand network services can help your enterprise scale connectivity, increase flexibility and support evolving AI workloads.
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