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For the past few years, most conversations around AI infrastructure have revolved around models, GPUs, and compute capacity. But as enterprises move from experimentation into real deployment, the reality that AI performance depends on more than compute alone is becoming clear.
The scale of investment reflects just how quickly this shift is happening. IDC recently reported that worldwide AI infrastructure spending reached $89.9 billion in Q4 2025 alone, up 62% year over year, with the market projected to surpass $1 trillion by 2029.
The industry conversation is expanding well beyond compute itself. Attention has shifted to the systems needed to deliver AI-ready digital infrastructure at scale, from connectivity and automation to orchestration, interoperability, and trusted collaboration across increasingly distributed environments. As organizations connect AI resources across clouds, data centers, edge locations, and enterprise infrastructure, Network as a Service (NaaS) is emerging as a foundational capability for delivering the flexible, automated connectivity they require.
Here are five signals shaping the next phase of AI-ready connectivity.
1. Connectivity Is Moving Back to the Forefront
For years, connectivity sat largely in the background of infrastructure conversations. AI is quickly changing that. AI workloads rarely stay put anymore. Training may happen in one environment, inference in another, while real-time applications depend on data moving continuously across clouds, enterprise infrastructure, edge locations, and specialized data centers.
That shift means moving data becomes just as important as processing it. Transport, latency, interconnection, and visibility all start carrying more weight.
Analyst firms including IDC and Omdia continue to highlight growing investment in AI infrastructure beyond compute itself. More attention is now being paid to networking, data movement, and the efficiency of the infrastructure underneath it all. Across the industry, cloud providers, digital infrastructure providers, data center operators, internet exchanges, cybersecurity companies, technology vendors, and communications service providers are all expanding investments tied directly to AI-driven traffic growth, distributed compute, and data center demand.
2. AI Is Stretching Infrastructure Across Ecosystems
One of the biggest shifts happening right now is the move away from centralized infrastructure assumptions. AI deployments rarely live in one place. They stretch across cloud providers, enterprise environments, colocation facilities, edge infrastructure, and increasingly, multiple service providers as well. Enterprises are unlikely to rely on a single environment for all AI-related workloads and services, especially as deployment models continue to evolve.
That flexibility creates opportunity, but it also creates complexity. Increasingly, AI workloads span independent organizations, not just multiple clouds or environments, requiring federation models that enable ecosystem partners to discover capabilities, establish trusted relationships, coordinate services, and maintain their own commercial relationships, operational independence, and governance.
Research from organizations including STL Partners and Analysys Mason points to growing coordination challenges as services extend across multiple platforms, domains, and providers. Once services cross multiple providers and platforms, even basic questions become harder to answer:
- Who owns performance?
- Where does visibility break down?
- How is service quality maintained across environments?
3. AI Is Raising the Stakes for Automation
For years, automation was positioned primarily as an efficiency initiative, but AI is changing the stakes.
As environments become more distributed and service demands become less predictable, manual coordination across organizations becomes much harder to scale. AI-driven applications introduce constant variability in traffic patterns, workload distribution, and service requirements. Traditional infrastructure management approaches were never designed for that level of dynamism.
Providers are quickly recognizing this. In NVIDIA’s 2026 State of AI in Telecommunications survey, 65% of telecom respondents said AI is already driving increased investment in network automation.
That changes the role automation plays inside infrastructure environments. It’s no longer viewed simply as a way to reduce operational overhead. Instead, it’s becoming essential to keeping complex environments responsive, scalable, and manageable as workloads move across systems and providers. These automation capabilities also form the foundation of NaaS, enabling organizations to dynamically provision, orchestrate, and adapt connectivity as AI workloads move across distributed infrastructure.
4. Fragmented Ecosystems Won’t Scale Easily
As infrastructure environments become more distributed, interoperability becomes less of a feature and more of a requirement for scale.
Periods of rapid technology change almost always introduce fragmentation. AI infrastructure risks accelerating that problem if ecosystems evolve independently. Scalable AI services will depend heavily on interoperability and coordination across environments that might include multiple providers, technology stacks, and operational domains.
In response, organizations across telecom, cloud, AI infrastructure, cybersecurity, and digital infrastructure ecosystems are aligning around open APIs, automation standards, trust frameworks, and federated operating models that allow independent organizations to collaborate without requiring centralized control. These capabilities will be critical to enabling scalable NaaS Federation.
5. Traditional Connectivity Expectations Are No Longer Enough
Enterprise expectations around infrastructure are changing alongside AI adoption itself.
Organizations are no longer evaluating connectivity based solely on bandwidth or uptime. As AI workloads become more dynamic and distributed, expectations are expanding as well. Enterprises want better visibility across services and infrastructure, more predictable performance, stronger resiliency, and automation that can adapt as workloads shift across environments.
More importantly, they expect these capabilities to work together consistently across providers and platforms. Increasingly, they also expect services to be discovered, provisioned, and managed seamlessly across organizational boundaries rather than within a single provider environment. That’s forcing enterprises to evaluate whether their infrastructure can support rapidly changing AI workloads, shifting traffic patterns, and real-time service requirements without introducing additional operational complexity.
That creates both opportunity and pressure across the ecosystem as communications providers, cloud providers, AI infrastructure companies, digital infrastructure providers, data center operators, cybersecurity companies, internet exchanges, and technology companies adapt to a market that demands greater agility, coordination, and responsiveness.
The Industry Is Moving from AI Demand to AI Delivery
Compute will remain central to AI's continued growth, but AI-ready digital infrastructure will ultimately depend on how effectively organizations coordinate services, automate operations, establish interoperability, and deliver connectivity through scalable NaaS. As AI workloads increasingly span independent organizations, industry progress will rely on open APIs, common standards, trust frameworks, Agentic LSO, and federation models that enable secure collaboration without sacrificing operational independence. These priorities are shaping the industry's next phase as it moves from AI experimentation to AI delivery at scale.
At Mplify, we are bringing the industry together to advance AI-ready digital infrastructure through Agentic LSO, NaaS Federation, open APIs, standards, and trust frameworks that enable secure collaboration across the global digital ecosystem.