AI's moment of disillusionment

"Linthicum has also taken serverless to task. "Serverless technology will continue to fade into the background due to the rise of other cloud computing paradigms, such as edge computing and microclouds," he says. Why? Because these "introduced more nuanced solutions to the market with tailored approaches that cater to specific business needs rather than the one-size-fits-all of serverless computing." I once suggested that serverless might displace Kubernetes and containers. I was wrong. Linthicum's more measured approach feels correct because it follows what always seems to happen with big new trends: They don't completely crater, they just stop pretending to solve all of our problems and instead get embraced for modest but still important applications."

"This is where we're heading with AI. I'm already seeing companies fail when they treat genAI as the answer to everything, but they are succeeding by using genAI as a complementary solution to some things. It's not time to dump AI. Far from it. Rather, it's time to become thoughtful about how and where to use it. Then, like so many trends before (open source, cloud, mobile, etc., etc.,) it will become a critical complement to how we work, rather than the only way we work."

Technological hype often produces inflated expectations followed by disillusionment across many innovations, including cloud computing, serverless, and generative AI. Cloud promises of productivity gains and cost savings have frequently underdelivered, leading to criticism. Serverless has not displaced other paradigms; edge computing and microclouds offer tailored solutions for specific business needs. Major trends rarely vanish; they stop being marketed as panaceas and instead find modest, important roles. Companies that treat generative AI as a universal fix tend to fail. Organizations succeed when they deploy generative AI as a complementary tool in targeted use cases.

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