Headlines remain focused on hyperscale AI training factories straining power grids and sparking community pushback. But a tectonic shift is reshaping the AI landscape.

• The center of gravity is moving from training in massive data centers to real-time inference — using trained AI models to generate outputs — at the edge.

Why it's important: As enterprise AI matures, processing must happen where data is generated and decisions are made.

Get up to date: Three trends are driving the shift to edge.

1️⃣ The shift to inference: The majority of AI workloads are moving from resource-intensive model training to ongoing, operational inference.

• AI inference is forecast to overtake AI training in 2027 — and poised to surpass both training and non-AI compute by 2028, according to McKinsey.

2️⃣ Edge migration: Compute, data and model execution are leaving centralized cloud environments for localized edge deployments.

• Gartner previously projected that 75% of enterprise-generated data would be created and processed outside of traditional data centers or the cloud by 2025.

3️⃣ Sovereignty and speed: Mission-critical industries require ultra-low-latency, real-time decision-making paired with strict data sovereignty and localization.

• Balancing these requirements has become a strategic priority for CIOs and CTOs, with 9 in 10 large enterprise C-suite leaders worried about implications for their job and company.

Okay, but: Proliferating AI to the edge dramatically expands the cyber attack surface.

• Deploying intelligence across countless users, devices and other endpoints creates significant cybersecurity vulnerabilities. CrowdStrike's 2026 Global Threat Report identifies edge devices as the preferred strategic access points for adversaries.
• Protecting distributed infrastructure requires a robust defense: zero-trust architecture combined with quantum-resilient encryption for data both in transit and at rest.

What you need to know: It's not just about a hybrid cloud shift from hyperscale campuses to edge. Pushing high-performance AI into urban and austere environments requires maintaining data sovereignty and implementing robust cyber protections.

• Regulatory requirements across jurisdictions are adding urgency to the need for secure edge infrastructure.

Next steps: Available Infrastructure is tackling this transition head-on with SanQtum.

• SanQtum is a fleet of sovereign, micro-node, edge AI fortresses engineered for rapid deployment, ultra-low latency and high-performance compute — all wrapped in zero-trust architecture with post-quantum defense.

Project Qestrel is the Phase 1 buildout, with plans for 1,000 sites across 100 U.S. cities spanning more than 30 states.

• SanQtum nodes are configured in three-site clusters for enhanced resilience and interconnected with intraregional dedicated fiber.
• Multiple cities are already live, with more than 200 sites actively under construction.

Learn more about how SanQtum and Project Qestrel are securing the future of edge AI.