To be frank, the tech community has really been leaving a sour taste in my mouth the last few years. Everyone is anxious to bandwagon on these buzzwords that provide little to no benefit or are applicable only to a small segment of the tech population.
In most cases, even a superficial understanding of the problem space should make it obvious that $This_Weeks_Sexy_Solution is a very bad fit. Do you need a server that persists data and is individually addressable? Then why are you using Kubernetes and Docker, which are still struggling to figure out these very basic things? k8s and Docker have very specific uses, but unless you're Google, they're probably not the right fit for your production environment right now.
This phenomenon seemed to hit a critical mass with document databases and single-page apps, and it continues to iterate with every open-source release that comes out of Google or Facebook. Since Google released TensorFlow last year and the compounded hype of [much worse than advertised attempts at] conversational speech recognition in Siri and Alexa, the "machine learning" bandwagon is starting to try to edge into the spotlight, and it sounds like it's already a mandatory part of any VC pitch.
Fortunately, machine learning is pretty hard and you get into hairy math practically right away, so I don't think the legs on this one will last as long. But we'll see. We'll at least have a lot of faux-ML going around and a lot of people making spurious claims on their resumes.
The economic collisions that make Silicon Valley and the tech industry in general a hive for inexperienced and insecure youth are bearing some really interesting effects this way. How can a company that's not "blown about by every wind of [tech fad]" fully exploit its relative sanity for competitive advantage?
[0] https://news.ycombinator.com/item?id=13572415 ; my reply downthread at https://news.ycombinator.com/item?id=13573978