Lessons learned from 6 overhyped tech trends
Bill Doerrfeld | January 5, 2026
My
latest on InfoWorld explores major tech trends that failed to deliver.
I don't know about you, but no fully immersive, transhumanistic reality ever arrived in my workplace.
My friends and I never ended up storing all our data in a globally distributed immutable ledger, either.
And no — I'm not spending my hard-earned money on non-fungible images of bored apes.
I have a lot of data, but I wouldn't call it "big." It's mostly sinking in various lakes that no one can access.
The hype cycles go on and on... so today I poke fun at six overhyped trends on InfoWorld.
Read the article to commiserate on past failures, and pull out the lessons that actually matter going forward.
More than bubble-bursting hot takes, the piece explores the psychology underlying tech's insatiable lust for the next big thing.
Like any production software application, AI agents are producing a spectrum of metadata behind the scenes. Some are calling agentic metadata a “gold mine” to direct continual improvements.
My latest DirectorPlus column with LeadDev interviews Bedrock Robotics' CTO, Kevin Peterson, on what it takes to develop highly adaptable and safe autonomous machines.
I explore some tips to help speakers craft solid pitches. The Nordic APIs speaker selection committee looks for these sorts of things, but the tips could apply to any tech event.
I made 10 predictions on how AI and APIs will converge in 2026. Signs point to AI agents being the next big API consumer.
My latest for InfoWorld breaks down 10 MCP servers powering next-gen devops workflows, from GitHub and Atlassian to AWS and Pulumi.
Excited to share my talk from Nordic APIs' Platform Summit 2025. As the opening talk of the event, I wanted to address the elephant in the room head-on: MCP.
Watch all the great API talks, keynotes, and panel discussions from this year's Nordic APIs Platform Summit, now available on YouTube.
Block's open source initiative is helping to build brand reputation, attract talent, boost partnerships, guide internal open source best practices, and aid long-term system reliability.
Achieving ROI with AI requires a mix of strong leadership stewardship upfront, shifting the talent framework, and embedding proper monitoring and governance throughout the lifecycle.
I recently explored what goes into creating a solid AI agent knowledge base — from the types of data it should contain to the retrieval mechanisms and architecture patterns that support reliable agentic behavior.
