Alexander Arvidsson

There’s a fundamental difference between cloud and on-prem work that I keep coming back to. Cloud is a sophisticated model kit with pre-designed pieces that snap together. On-prem was a pile of Lego bricks and a problem to solve. Everything in between was yours to figure out. If I had to go back tomorrow, I know exactly what skill would need the most work. It’s not the technical stuff you’d guess. It’s something more fundamental, something that years of managed services have slowly let atrophy.

Your brain evolved to detect lions. Now it may fire every time you open ChatGPT. This post unpacks three reasons LLMs are not neutral tools — what they’re trained on, how RLHF creates systematic pressure toward validation, and what your neurobiology does with the result. Then it gets specific: the same sycophantic system creates meaningfully different failure modes depending on who is using it. Nobody designed this. Nobody fully planned for it. And most deployment practices still aren’t accounting for it. The research is early. The mechanisms are not.

This week I’m heading to Prague for DataPoint, organized by my friend Štěpán Rešl, a man whose brain capacity I genuinely cannot account for. Quick trip: arrive Thursday, deliver my data literacy session Friday morning, fly straight home. No time to explore the city, which I’ve been promised is beautiful. I can confirm the food is extraordinary. I’ve been thinking about one particular meal since 2019. New post on what I’m delivering and why the topic still matters.

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.