By Guest: Ari Weil (LinkedIn)
Company: Akamai
Show Name: An Eye on AI
Topic: Edge Computing
AI is no longer bottlenecked by model training. The real constraint is inference — how fast intelligence can be delivered where decisions actually happen. As applications become real time, agentic, and machine-driven, centralized AI architectures are starting to break.
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In this clip, Ari Weil, VP of Product Marketing at Akamai, explains why inference at the edge is becoming foundational to the next phase of enterprise AI, and how Akamai Inference Cloud is designed to meet that shift.
From static content to real-time intelligence
For decades, the internet was optimized for static content delivery. Pages were cached, assets were distributed, and performance was about proximity.
According to Weil, AI changes that model entirely. Content is no longer static — it is generated dynamically, in real time, through compute. That shift fundamentally alters how infrastructure must be designed.
Inference, not training, is where AI becomes useful. Enterprises may build intelligence in centralized AI factories, but value is only realized when that intelligence is applied instantly — close to users, devices, and machines interacting with data.
Why inference belongs at the edge
Latency, cost, and scale are forcing a rethink of centralized AI deployments. Real-time use cases — personalization, video intelligence, fraud detection, autonomous systems — cannot tolerate round trips to distant data centers.
Akamai’s approach moves dense GPU compute closer to where data is created. By combining its global edge footprint with Nvidia’s enterprise AI architecture, Akamai Inference Cloud allows enterprises to route inference requests to the right model, on the right infrastructure, at the right location. This enables faster responses, lower transit costs, and more predictable performance.
Inference as the bridge to agentic AI
A key theme Ari highlights is the industry’s shift toward agentic and machine-to-machine interactions. As AI systems begin to reason, plan, and act autonomously, inference workloads become more complex. Multi-step inference and long-running reasoning chains require distributed orchestration, not monolithic platforms.
Edge-based inference enables this by coordinating context, capacity, and decision routing in real time. Instead of sending every request back to a central system, intelligence is applied where it is most effective — turning AI from a back-end capability into an operational layer of the internet.
Enterprise impact: performance, cost, and flexibility
Beyond performance gains, inference at the edge directly impacts enterprise economics. Model routing, semantic caching, and intelligent orchestration reduce unnecessary compute cycles and data egress. Open-source foundations and multi-cloud portability ensure enterprises are not locked into a single provider or architecture.
This flexibility matters as AI workloads span clouds, regions, and physical environments. Akamai’s strategy reflects a broader reality: enterprise AI will not live in one place. It will be distributed by necessity.
What this signals for decision-makers
For platform teams and executives, the takeaway is clear. The future of AI infrastructure is not just bigger models or faster training. It is about inference at scale — securely, cost-effectively, and close to where intelligence is consumed.
Akamai Inference Cloud represents a shift toward AI-native application networks, where real-time intelligence becomes a default capability rather than an optimization.
