Guest: Ari Weil (LinkedIn)
Company: Akamai
Show Name: An Eye on AI
Topic: Edge Computing

As AI systems become more real time and increasingly agentic, the biggest constraint is no longer model quality. It’s latency, proximity, and orchestration. Enterprises are discovering that where inference runs determines whether AI delivers value at scale.

In this clip, Ari Weil, VP of Product Marketing at Akamai, explains how moving inference to the edge transforms performance, security, and cost — and why this shift is foundational to the future of enterprise AI.

Latency turns AI from insight into action

Real-time AI depends on milliseconds, not minutes. Ari points out that one of the edge’s biggest advantages is its ability to coordinate and orchestrate inference instantly based on user interaction. Routing a request to the right model, the right infrastructure, and the right application in real time is what allows AI systems to respond intelligently instead of lagging behind events.

By deploying GPU compute close to where users and machines interact with data, Akamai enables enterprises to reduce round trips to centralized infrastructure and deliver outcomes faster and more reliably.

Security and orchestration at the edge

As AI applications grow more dynamic, security becomes inseparable from performance. Edge-based orchestration allows enterprises to apply security controls, context, and policy decisions close to execution, rather than exposing data to unnecessary transit and centralized bottlenecks.

This distributed model supports dynamic, real-time content generation — a major shift from the static asset delivery that defined earlier internet architectures.

Why multi-cloud portability matters

Ari emphasizes that AI will not be built on a single cloud or a single provider. Scaling AI infrastructure requires portability across clouds, regions, and physical locations. Akamai’s cloud, built on open source foundations, is designed to support this reality by enabling AI workloads to move where they make the most sense for performance, cost, and compliance.

This approach allows enterprises to orchestrate intelligence across heterogeneous environments while avoiding lock-in and preserving flexibility.

From static networks to intelligent application platforms

Over the last 25 years, Akamai has evolved from delivering static content to adding security, edge computing, serverless, and stateful workloads. With inference at the edge, those capabilities converge into a real-time, intelligent application network.

By integrating data fabrics, storage options for embeddings and core data, hosted and external models, and a rapidly expanding global footprint, Akamai is positioning inference as a native capability of the network itself.

What this means for enterprise leaders

For platform teams and executives, the takeaway is straightforward. Real-time, agentic AI requires distributed inference, intelligent orchestration, and proximity to users and machines. Centralized AI alone cannot meet these demands.

Edge inference is no longer an optimization. It is the foundation for scalable, secure, and cost-effective AI systems that are built for how enterprises operate today — and where they are headed next.