Edge computing burst onto the tech scene several years ago with great fanfare and promise. It was heralded as a revolutionary new paradigm that would change computing as we knew it. By pushing computation and data storage closer to the “edge” where local devices and sensors reside, rather than relying solely on the cloud, edge computing aimed to reduce latency, enable real-time data processing, improve reliability, and enhance security. The potential benefits for IoT, AI, AR/VR, autonomous vehicles, and other emerging technologies were tremendous.

However, the heady optimism surrounding edge computing has dampened somewhat in recent years. The technology has not yet lived up to its full disruptive potential. There are several key factors that help explain what happened to edge computing:

1. Complexity of implementation – While conceptually straightforward, actually deploying and managing edge networks at scale has proven difficult. Integrating heterogeneous devices, optimizing latency across geographies, allocating workloads, and orchestrating connectivity across edge and cloud introduces daunting complexity. The lack of unified standards and best practices has hindered adoption.
2. Cost – Building out distributed infrastructure across metro areas and globally requires enormous capital expenditure. For many companies, the costs have outweighed the promised benefits thus far, especially given the immaturity of edge-enabled use cases. The edge business case remains unproven.
3. Skills gap – Extracting value from edge networks requires expertise in OT/IT convergence, data engineering, middleware, device management, and more. Talent with this precise mix of skills has been hard to come by. Many organizations lack the right in-house teams to deploy edge computing.
4. Security risks – Pushing out compute power to the edge expands the attack surface dramatically. New edge-specific vulnerabilities combined with inadequate security measures have exposed networks to greater risk. Not enough attention has been paid to securing edge devices and infrastructure.
5. Regulation – Strict data sovereignty and privacy regulations in some regions have made it difficult to deploy centralized, cloud-reliant edge networks. As edge use cases like self-driving cars and smart factories emerge, regulatory uncertainty persists.

In summary, while edge computing is still in its early stages, the hype has cooled as pragmatic challenges have surfaced. However, the core value proposition remains compelling. As technologies and skills mature, edge is likely to proliferate in clearly defined, high-value use cases. But it may take time for edge computing to truly become the seismic shift many predicted. Careful management of complexity, costs, security, and compliance is needed to unlock the full potential of this important innovation.