Connected Intelligence: Wired for AI
From power-hungry DCs to ambient edge, four shifts reshaping the rules of networking
Much "ink" has been spilled over AI's gourmand appetite for compute, something akin to Pac-Man on a speed boost. In the process, it’s rewriting the blueprint for how networks are built, scaled and monetized. From GW-scale data centers to proximity compute at the edge, the demands of real-time intelligence are forcing a rethink across the entire networking stack.
These four shifts reveal where the future is headed, and what it means for communication service providers (CSPs) and networking hardware and software providers globally.
1 | The GW‑scale buildout is the new baseline
Move over megawatts, AI infrastructure is now being measured in gigawatts.
OpenAI’s Stargate initiative, in partnership with Oracle, recently committed to an additional 4.5 GW of data center capacity in the U.S.
Far from incremental growth, this is a redefinition of scale.
Why it matters
Power is the new scarce resource. Securing reliable, low-cost energy and negotiating utility partnerships are now central to any data center strategy.
Location and land matter. Hyperscalers are racing for grid-adjacent, regulation-friendly real estate.
Network becomes mission-critical. These AI superclusters demand non‑blocking, low-latency fabrics so thousands of GPUs can talk to each other at full line rate.
For CSPs this surge in compute demand is both a headache and an opportunity. They must evolve beyond “dumb pipes” to offering high-bandwidth wavelength services (100G → 400G → 800G), GPU-as-a-Service and edge AI hosting.
For vendors, the mandate is clear: build ultra-low latency, high-capacity switches, software that automates operations at scale and security drawn around AI workloads.
2 | Edge + 5G + wearables = intelligence everywhere
The age of centralized compute is giving way to a model of distributed intelligence. AI inference and real-time decision-making is migrating to the network’s edge: to cell sites, local data centers, even wearables. This to meet the sub‑millisecond demands of AR/VR, robotics and smart systems.
Qualcomm and others argue that traditional OS/app frameworks will recede into the background; AI will become the operating layer itself. (Think: your headset, earbuds or glasses managing context and intent locally.)
What shifts for players
CSPs can monetize their geography: deploying private 5G, slicing network contexts, embedding compute into their footprint. They cease being mere transporters and become enablers of localized AI.
Vendors must deliver compact, rugged, high-density hardware and orchestrated software that spans from core to edge, all managed via APIs and intent-based control.
3 | Grok and the invisible infrastructure war
Grok’s builder-first strategy signals a deeper shift: engineering first, eyeballs second. It’s embedding AI where work actually happens, not where users merely consume it.
Natural language becomes the interface. Code becomes the output. And AI becomes the collaborator.
That user-facing shift depends on invisible infrastructure: sub-100 ms inference pipelines, context caching, fast routing and ultra-close compute. In other words, the infra itself becomes a product.
The battleground has moved solely from model performance to the infrastructure layer on which developers write, run and scale AI quickly.
The winners won’t just be good LLMs. They’ll be the models and platforms that deliver programmable, intelligent networking and compute fabrics, with developer-level control baked in.
4 | Amazon’s quiet de-centralization: the cloud continuum
Amazon’s posture is sometimes misread. They’re not abandoning the cloud. They’re redefining the cloud as a continuum, from core to edge, to you.
The drivers
Latency, data gravity, regulation make remote compute untenable for many workloads.
Amazon is embedding compute deeper into the network (Outposts, Local Zones, Wavelength).
This plays into the CSP narrative: their assets (telco real estate, fiber and presence) become essential nodes in that continuum.
For CSPs, this is validation. They must position as the platform, not the underbelly. Integration, assurance, identity-aware policy enforcement and per-session visibility. These aspects become their differentiators.
For vendors, this means converging cloud and edge designs. Software must tie disaggregated compute, identity, telemetry and policies into a consistent fabric, regardless of location.
Final Thought: Networks are the Product. Plain and simple.
We’re in a moment where compute, data and traffic are collapsing into a single, AI-driven continuum across control and management planes. In the near future, AI’s ubiquity may render terms like “AI-defined networking” obsolete.
For CSPs: the mission has evolved from “carry everyone’s traffic” to “orchestrate compute where it matters most” while delivering intent‑aware connectivity and monetizing real estate, trust and identity.
For hardware/software vendors: size and speed are still battlegrounds, but so, too, are autonomy, observability and programmability. And, increasingly, the ability to embed AI into the network fabric, from edge to core.
The builders who treat networks as both AI’s front line and its foundational backend will be the architects of the era of connected intelligence.
