The Inference Era Is Here
— And It Will Reshape the Connectivity Industry
The AI conversation has entered a new chapter. For the past three years, the narrative was dominated by training: massive GPU clusters, billion-dollar data centers, and an arms race for raw compute power. That chapter isn’t over, but a far more consequential story is now unfolding — one that places connectivity providers at the very center of the AI economy. The inference era has arrived, and it’s moving to the edge.
From Centralized Training to Distributed Intelligence
Inference workloads will account for roughly two-thirds of all AI compute in 2026, up from a third in 2023 and half in 2025, according to Deloitte. The signs point to 2026 being the breakout year of AI inferencing. And unlike model training, which thrives in centralized hyperscale environments, inference demands something fundamentally different: proximity.
As Connectbase Founder and CEO Ben Edmond recently put it in a conversation with INCOMPAS CEO Chip Pickering, “Not all of us want to work in a skyscraper downtown. Some use cases for offices and retail belong in suburbia and rural, and inference is no different.” The analogy is apt. While centralized data centers optimized for cost-per-token will continue to serve specific workloads, an entirely new class of AI applications requires low latency, local governance, and physical proximity to the end user.
Consider radiology. As Edmond explained, a network of 50 radiology clinics across Western Pennsylvania cannot route AI-powered diagnostics through a data center in the middle of Iowa. Those clinics need edge data centers processing locally — maintaining a 5-millisecond latency band on the round trip to execute the model properly. That’s not a cost optimization play. That’s a use-case delivery imperative.
The Edge Demand Drivers Are Multiplying
The market data validates what forward-thinking providers already see on the ground. The global AI inference market is projected to grow from $117.80 billion in 2026 to $312.64 billion by 2034. The edge inference segment is expected to lead the market, contributing 70.76% globally in 2026, driven by increasing demand for real-time, low-latency AI processing.
The global edge data center market is expected to grow from $16.9 billion in 2026 to $71.9 billion by 2035, at a CAGR of 17.5%. And the capital flowing into AI infrastructure writ large is staggering. The five largest US cloud and AI infrastructure providers have collectively committed to spending between $660 billion and $690 billion on capital expenditure in 2026, nearly doubling 2025 levels.
The demand drivers Edmond identified are already materializing. Autonomous vehicles — arriving in cities like Boston — require AI inference at the point of action, not 200 milliseconds away in a remote facility. Healthcare continues to push edge requirements for patient-proximate AI models. Fraud detection is migrating toward edge processing because speed of response directly correlates to loss prevention. And the content ecosystem, with AI-powered personalization now running at the device level, demands the kind of distributed intelligence that only edge infrastructure can deliver.
At NVIDIA GTC 2026, leading operators in the U.S. and Asia announced AI grids — geographically distributed and interconnected AI infrastructure — using their network footprint to power and monetize new AI services across the distributed edge. This is not theoretical. The shift is underway.
The Connectivity Industry’s Moment of Truth
Here’s the reality that too many providers are still not grasping: physical infrastructure alone is not the end game. The winners in the inference era will be those who combine physical networks with digital truth — the ability to present, transact, and deliver in real time.
Edmond is unequivocal on this point. “Data centers, power infrastructure, fiber operators, and network providers as a whole need to have a real-time digital twin mindset,” he said. “The world’s moving too fast. Those use cases can’t wait 90 days — they can’t wait for manual processes of ‘let me check capacity.'” The connectivity industry must deal with 129 million unique structures in the United States alone. That complexity cannot be managed through spreadsheets and legacy CRMs. It requires location truth as an operational foundation.
This is where the concept of transactional readiness becomes critical. In an AI-driven economy, machines are communicating and transacting — not just people. The speed at which a provider can convert a quote to an order, verify serviceability, and provision connectivity is a direct competitive advantage. Edmond frames the industry’s current challenge bluntly: “We’re one of the few industries where you can place an order and 30 to 120 days later find out you can’t actually get what you signed a contract for.” That fallout problem erodes revenue, damages trust, and leaves opportunity on the table.
Sovereignty, Trust, and Monetization
The inference era introduces another dimension that connectivity leaders must internalize: sovereignty. As AI workloads distribute across edge nodes, the question of where models execute, where data traverses, and which jurisdictions are involved becomes a compliance imperative and a monetization opportunity.
As Edmond observed, if a network routes traffic from Dallas to Iowa to Seattle to Toronto to New York and crosses a national boundary, compliance with sovereignty requirements is broken — even if the start and finish land in the right jurisdiction. Providers who understand and control for that location implication can create value in the same way that SLA guarantees and low-latency paths already command premium pricing. The financial industry has demonstrated this model: low-latency paths have been monetized at three to ten times more than a standard route between the same two points. AI inference creates similar opportunities for providers who think beyond commodity connectivity.
Preparing for What’s Next
The next 12 to 24 months will separate the providers who are ready from those who are not. The formula is not complicated, but it requires conviction. Invest in location truth as the foundation for every operational process. Digitize for connected commerce — because if you’re taking a traditional approach to monetizing the AI infrastructure of the future, you’re missing the opportunity. Build for sovereignty as both a compliance requirement and a value-creation strategy. And recognize that no single provider owns everything everywhere. The AI infrastructure opportunity demands an ecosystem — a connected fabric of partners, networks, and facilities that can transact with each other seamlessly.
This is the vision that Connectbase, the global ecosystem for buying and selling connectivity, was built to enable. With 2.7 billion serviceable locations across 150+ countries and 400+ providers, the Connected World platform provides the location intelligence, transactional readiness, and ecosystem connectivity that the inference era demands.
The AI wave is the biggest network shift we’ve seen. The providers who move now — who digitize, who build trust into the DNA of how they operate, who participate in a connected ecosystem — will capture the value. Everyone else will be left wondering what happened.
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