Training

Data sovereignty concerns - driven by the CLOUD Act and NIS2 obligations - are pushing Swedish organizations to reconsider on-premises analytics. The problem: a decade of cloud adoption has quietly eroded the skills needed to execute on-prem projects. The Microsoft stack in 2026 is more capable than most assume, but lacks a Fabric equivalent. The real decision is operational ownership, not technology preference. Organizations that can reason across the full cloud-to-on-prem spectrum will navigate the uncertainty best.

The tech industry is cutting junior roles at record pace, calling it AI efficiency. But here’s the problem: you can’t skip a generation of practitioners and expect institutional knowledge to survive. The seniors who remain were once juniors who had space to build understanding from the ground up. That pipeline is being shut off. Part 2 is about what to do about it. How to mentor like you prompt. How to train for reasoning, not just execution - and why this might be the best time to hire juniors, not the worst.

A Wharton study put 1,372 people in front of a deliberately wrong AI. 73% followed it anyway. Not from carelessness - from fluency, confidence, and the absence of a trained skeptical reflex. We built that gap ourselves: ten years of certifications that taught procedure and skipped judgment. Now the procedures belong to agents, and the engineers we trained have no independent model to cross-check against. This is about what we teach instead - and why broken things are the best place to start.