AI Means Edge; Edge Means 5G

Artificial intelligence might prove to be a very-important driver of incremental revenue growth for mobile operators in the 5G era, both directly and indirectly.

Most would agree that what "internet of things" really means is pervasive computing, conducted by scores of devices in every house and many devices ambient with the people who use devices. So computing will be done all over the place.

At the same time, AI will be key to creating value out of all the unstructured big data created by apps, users and devices.

So we have huge numbers of smart and communicating devices, supporting apps that create huge amounts of data. AI creates value by extracting insight (patterns and predictions) from all that data.

Also, everyone would agree that AI is computationally intense.

For many use cases, there will literally not be time (latency will matter) to base the apps on use of traditional cloud computing centers. Instead, processing will have to be done "at the edge."

And that is where new roles could emerge for mobile operators and others. The need to support AI-using devices often will mean a need for distributed edge computing.

Edge computing, in turn, will create new value for locations scattered around the network that can do the processing, and mobile operators have some advantages (real estate, power sources, high bandwidth connectivity, low-latency networks, incentives to grow a role in edge computing and applications requiring edge computing) that could be leveraged to create a role in the new business.

That is, in part, why 5G networks will feature high bandwidth and low latency, but also might require use of small cell architectures that put many new potential nodes out in the network.
So, though it is not always obvious, AI could enable new sources of value, business models and revenue for mobile operators.

It is fairly easy to see how artificial intelligence (AI) is a benefit for app and device suppliers. To use the obvious examples, voice interfaces and customization of content are applied examples of AI.

And though AI enables features, not necessarily full business models, the issue is whether, as mobile operators attempt to move “up the stack,” AI can help, and if so, how?

At least one line of reasoning is that pervasive computing requires AI; which requires edge computing; which requires high-bandwidth, low cost, low latency networks. That is sort of obvious.

The big challenge is whether the shift to edge computing can be used by mobile operators to support "move up the stack" initiatives where "computing services" become part of the "communications service."



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