We still haven’t learned

This letdown isn’t just an AI thing. We go through this process of inflated expectations and disillusionment with pretty much every shiny new technology. Even something as settled as cloud keeps getting kicked around. My InfoWorld colleague, David Linthicum, recently ripped into cloud computing, arguing that “the anticipated productivity gains and cost savings have not materialized, for the most part.” I think he’s overstating his case, but it’s hard to fault him, given how much we (myself included) sold cloud as the solution for pretty much every IT problem.

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.