I have spent close to two decades building products and technology from India for global markets: the United States, Europe, and Asia. Distance from the user raises the bar rather than lowering it. A customer in New York or London does not care where a platform was built; they hold it to the standard of anything built next door, and my teams and I were measured against exactly that. There is no pass for the distance. Understanding users and markets you do not live in is part of the discipline, and it is the harder part.
The route ran through very different rooms: enterprise systems at a digital industrial company, a global media network, a century-old publishing house, a private-equity-backed data SaaS business, and now a venture of my own in the agentic AI space, with consulting alongside. I am an engineer by training and an MBA by choice, so I argue for technology in the language of return, and question business cases in the language of engineering. Watching which lessons transfer between those rooms, and which each industry has to relearn its own way, is where most of my judgment comes from.
The technology has turned over more than once in that time: consumer platforms serving hundreds of millions of monthly users, and holding around a million of them at once on their peak nights, the streaming and data build-outs behind them, a generative AI product shipped to revenue while the category was still forming, and lately coding agents and an AI-native development standard across a hundred-plus person engineering organisation. The lesson that survived every wave is the one this publication is built on: the technology changes, and the job of deciding where it belongs does not.
Why I write in public
I am writing in public because I am still working it out, and because the people I most want to hear from are the ones going through it now, not the ones selling a finished answer.
The belief underneath it is unexciting on purpose. AI is a tool. A serious one, which means it deserves neither worship nor cluelessness. It deserves judgment. So I write about where I am finding it adds value, where I am finding myself stuck, and what I would decide differently now.
It will not only be about AI. Leading teams is my trade: people whose growth I am responsible for, stakeholders who depend on what gets delivered, most of it inside the operating model of a global capability centre, which has nuances of its own that rarely get written about honestly. So the leadership journey will show up here too. How I have changed over time, what the day-to-day teaches that the long view misses, what building something of my own is teaching me now, and how to carry the uncertain stretches without drama. Not a diary, and not everything that crosses my mind. The thoughts I have spent real time on, worked into something you can use, and only when they are worth yours.
Start here
Start with The Job Is Deciding Where AI Belongs: three questions that decide whether a piece of work goes to a machine, a tiering that decides how much of it, and four cases from my own operating record of what each decision cost. If you read one thing here, read that.
Each of those cases will get its own fuller anonymised write-up, with the numbers kept and the identifying details out, and the reasoning left out of the short versions. There is no fixed schedule here and no dates on those; a piece goes out when it is ready, not when a calendar says so.
Who this is for
If you are somewhere between the stampede and the freeze and looking for a way through that is neither, this is written for you. If you try any of it and it does not work the way I have described, that reply is more useful to me than agreement, and it usually makes the next piece better.
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