Posts

I’ve been going down a rabbit hole this week that started with a simple question: what’s the difference between Dronacharya and a drone? Turns out - not as much as you’d think. And the gap that does exist is the most important question in AI ethics right now. Drona was the master weapons instructor in the Mahabharata. Greatest military mind of his age. He trained Arjuna, the perfect archer. He commanded armies. He deployed weapons so powerful they couldn’t be recalled once released. Divine missiles that would travel to their target regardless of what happened to the hand that launched them. ...

How I Got Prompt Engineering Wrong At my work, one of my colleagues (whose age shall not be discussed publicly, though we regularly joke he may have been around during the Jurassic age) has unexpectedly become one of the most effective Claude users I know. While I was building carefully engineered prompts full of instructions, formatting rules, context, edge cases, and constraints, he would casually type things like: “Give me everything about topic X.” ...

Here is a real story. Back in early 2025, one of my very AI forward colleagues and I were talking about where all this was heading. He was convinced that by the end of 2026 AI would already have dramatic reach across society. The paranoia was real. Jobs gone. Massive disruption. Entire industries changing overnight. I had a different view. Not because I thought the technology would fail. Quite the opposite. I thought the technology would become capable of doing almost everything surprisingly quickly. ...

I finished high school in Delhi in 1999. That year still sits somewhere in the background of everything that came after it. Not sharply. More like a low hum. The world felt like it was shifting, but nobody had a clear map of where it was going. Y2K was part of that atmosphere. Not in a precise way. More like background tension. Computers might fail. Systems might misread time. Something about the infrastructure of modern life felt slightly untrusted, even if nobody could quite point to where the risk actually was. ...

In the previous posts, I wrote about speaking at Global Azure ANZ 2026 and the strange speed at which universities seem to be reversing themselves on AI. But the conversation that lingered longest in my head afterwards was about coding itself. Or maybe not coding exactly. More about the feeling of coding. One developer told me they just were not enjoying the new AI coding tools very much. The outputs were fine. Useful even. But something felt missing. The satisfaction had thinned out somehow. ...

In my previous post, What I Learnt Speaking at Global Azure ANZ 2026, I mentioned a short conversation that stayed with me long after the event ended. A university student told me that in 2023 they had been actively discouraged from using AI during study. Minutes later, another student casually described using AI throughout nearly everything they worked on. Those two conversations happened back-to-back. And honestly, they captured something important about the current moment better than most keynote presentations do. ...

I went to Global Azure ANZ 2026 expecting cloud talks and AI demos. The talks were good. But the most interesting part of the event happened in conversations between sessions. I had the opportunity to speak at the Wellington chapter of Global Azure ANZ 2026, hosted alongside the Wellington Data Management and Analytics Meetup. Like many community events in Wellington, the atmosphere was relaxed, practical, and refreshingly free of corporate theatre. What stood out most though was not a specific demo or benchmark. It was the feeling that the industry has quietly crossed another threshold. ...

उलझे नहीं तो कैसे सुलझोगे, बिगड़े नहीं तो कैसे संभलोगे If you never get tangled, how will you learn to untangle? If things never go wrong, how will you learn to set them right? I was part of three AI trials inside a New Zealand government agency. All three gave us invaluable lessons. Out of those three, one progressed quite a lot, through four stages of development, and has shown genuine promise. ...

Your organisation has a document management system. Maybe it’s called Objective. Maybe it’s TRIM. Maybe it’s a home-grown records platform that nobody wants to touch because the person who built it left in 2014. Whatever it’s called, it holds years of institutional knowledge. Briefings, reports, decisions, correspondence, approvals. The kind of material that makes new work possible — if you can find it. You can’t. Not quickly, anyway. And that’s the problem this post is about. ...

Most companies don’t fail at AI because of bad models. They fail because they never had a strategy. That might sound blunt, but it’s what shows up again and again in enterprise settings. Teams jump into pilots, experiment with tools, hire a few data scientists—and then stall. Not because the tech didn’t work, but because the system around it didn’t exist. If you’re leading AI inside an organisation, you don’t need more tools. You need structure. ...