Three issues with generative AI still need to be solved
Disclosure: Qualcomm and Microsoft are clients of the author.
Generative AI is spreading like a virus across the tech landscape. It’s gone from being virtually unheard a year ago to being one of, if not the, top trending technology today. As with any technology, there are issues that tend to surface with rapid growth, and generative AI is no exception.
I expect three main problems to emerge before the end of the year that few people are talking about today.
The critical need for a hybrid solution
Generative AI uses massive language models, it’s processor-intensive, and it’s rapidly becoming as ubiquitous as browsers. This is a problem because existing, centralized datacenters aren’t structured to handle this kind of load. They are I/O-constrained, processor-constrained, database-constrained, cost-constrained, and size-constrained, making a massive increase in centralized capacity unlikely in the near term, even though the need for this capacity is going vertical.
