Alexander Arvidsson

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.

Most speakers spend weeks building a presentation and zero minutes thinking about how to end it. Then they hand the microphone to the audience and call it a close. That’s not a close - it’s an abdication. The serial position effect means your ending is the thing most likely to survive in memory. This post breaks down why Q&A endings undermine you, why stories work at a neurological level, and what the research actually recommends instead.

SQLBits is in Newport, Wales this year - a place that requires actual planning to reach, and a useful reminder that modern travel infrastructure is more remarkable than we give it credit for. I’m delivering two sessions: a workshop on data communication with Valerie Junk, covering why insights fail to drive action and what to do about it, and a Fabric FinOps session on what Microsoft Fabric workloads actually cost when you measure outcomes, not just Capacity Units.

You can delete a post. You can unpublish an article. You can invoke GDPR and demand erasure. What you cannot do is remove something from an LLM. Once data dissolves into billions of model weights, there’s no row to delete, no file to erase. And it gets worse: the models now training on AI-generated outputs are degrading with each generation, narrowing toward a statistical echo of themselves. The right to be forgotten has no technical implementation path. None.

Goodhart’s Law - when a measure becomes a target, it ceases to be a good measure - is quietly undermining data strategies everywhere. Support tickets get closed without being solved. Active users log in and leave. Velocity numbers climb while codebases rot. The problem isn’t bad metrics; it’s the assumption that better metrics fix the underlying issue. The real question to ask before deploying any KPI: if we optimize for this, what will break?

AI tools force knowledge workers into constant task-switching that neuroscience proves is cognitively expensive. Only 2.5% of people can effectively multitask, yet we’re asking everyone to perform like fighter pilots without selection or training. Berkeley researchers found workers experienced burnout as managers consumed efficiency gains with increased assignments—repeating the Jevons Paradox from the telegraph and typewriter eras. Organizations measure output volume while ignoring cognitive sustainability, creating hidden supervisory labor that extracts maximum throughput regardless of cost.