oud giants eye a potential windfall in AI at the network edge
David Linthicum is enjoying a well-earned “I told you so.”
Six years ago the chief cloud strategy officer at Deloitte Consulting LLP predicted that the then-fledgling edge computing market would create a significant growth opportunity for cloud computing giants. That went against a popular tide of opinion at the time that held that distributed edge computing would displace centralized clouds in the same way personal computers marginalized mainframes 30 years earlier. Andreessen Horowitz LP General Partner Peter Levine summed it up with a provocative statement that edge computing would “obviate cloud computing as we know it.”
Not even close. There’s no question that edge computing, which IBM Corp. defines as “a distributed computing framework that brings enterprise applications closer to data,” is architecturally the polar opposite of the centralized cloud. But the evolving edge is turning out to be more of an adjunct to cloud services than an alternative.
Deloitte’s Linthicum: “Edge computing drives cloud computing.” Photo: SiliconANGLE
That spells a big opportunity for infrastructure-as-a-service providers such as Amazon Web Services Inc., Microsoft Corp.’s Azure and Google Cloud Platform. Most researchers expect the edge market will grow more than 30% annually for the next several years. The AI boom is amplifying that trend by investing more intelligence in devices ranging from security cameras to autonomous vehicles. In many cases the processing-intensive training models that are needed to inform those use cases require supercomputer-class horsepower that is beyond the reach of all but the largest organizations.
Enter the cloud. International Data Corp. estimates that spending on AI-related software and infrastructure reached $450 billion in 2022 and will grow between 15% and 20% annually for the foreseeable future. It said cloud providers captured more than 47% of all AI software purchases in 2021, up from 39% in 2019.‘Cloud providers will dominate’
“We’re likely to see the cloud providers dominate these AI-at-the-edge devices with development, deployment and operations,” Linthicum said in a written response to questions from SiliconANGLE. “AI at the edge will need configuration management, data management, security, governance and operations that best come from a central cloud.”
There are good reasons why some people doubted things would turn out this way — starting with latency. Edge use cases such as image recognition in self-driving cars and patient monitoring systems in hospitals require lightning-fast response time and some can’t tolerate the time needed to round-trip requests to a data center a thousand miles away.
AI applications at the edge also require powerful processors for inferencing or making predictions from novel data. This has sparked a renaissance in hardware around special-purpose AI chips, most of which are targeted at edge use cases. Gartner Inc. expects more than 55% of the data analysis done by deep neural networks will occur at the edge by 2025, up from less than 10% in 2021.
But most edge use cases don’t require near-real-time responsiveness. “For adjusting the thermostat, connecting once an hour is enough and it’s cheaper and easier to do it in the data center,” said Gartner analyst Craig Lowery.
“Latency isn’t an issue in most applications,” said Mike Gualtieri, a principal analyst at Forrester Research Inc. “You can get a very reasonable tens of milliseconds of latency if the data center is within 100 or 200 miles. Autonomous driving demands extreme locality, but those aren’t the use cases that are driving mass deployment today.”Gold rush
Cloud providers stand to get much of that business, and they’re aggressively investing in infrastructure and striking partnerships to cement their advantage. Factors such as latency and edge intelligence will continue to exclude the big cloud providers from some applications, but there is so much opportunity in other parts of the market that no one is losing sleep over them. Even latency-sensitive AI applications at the edge will still require frequent updates to their training models and cloud-based analytics.