Build With Intent
Practical notes on tech leadership, AI platform building, and human-centred design — published every Friday.
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For years, software teams have treated coding speed as one of the biggest constraints in product development. We hired more developers. We created better frameworks. We automated deployments. We built reusable components. And now AI is dramatically accelerating the part we spent years trying to optimise. A developer ca
Every company seems to be chasing AI productivity right now. Can we write the report faster? Can the developer ship the feature faster? Can customer support answer more tickets? Can finance close the month quicker? Can the recruiter screen more CVs? All useful. But I’m increasingly convinced that this is not where the
For most of my career in technology, writing software was expensive. You needed developers. Developers needed time. Features had to be designed, coded, reviewed, tested and deployed. Even a relatively small experiment carried a cost. AI has changed that equation remarkably quickly. Today, a developer can describe a fea
For the last couple of years, businesses have been asking: “How can we use AI?” Then it became: “What can we automate?” Now the conversation is rapidly moving towards: “Where can we deploy AI agents?” Sales agents, HR agents, customer support agents, finance agents, recruitment agents, research agents — we are effectiv
The best AI product in your stack might never say a word. There's a pattern I keep seeing across every industry I work in. A business decides to "add AI." Six weeks later there's a chat box. It has a name. Sometimes a personality. Often a small avatar in the bottom right corner of the screen. It says "Hi, I'm Nova! How
And most teams are measuring everything except this. There is a moment in every product's life that tells you more about product-market fit than any survey, NPS score, or retention metric ever will. It's the moment you watch a real user use your product for the first time. Not in a usability test. Not in a structured d
And why most enterprise AI deployments have this completely backwards. There's a moment I keep seeing play out across enterprise AI deployments. A system produces an output. The output is wrong — confidently, fluently, plausibly wrong. And the only thing standing between that wrong output and a consequential decision i
On Consistency, Availability, Partition Tolerance — and the constraint Eric Brewer never named. In 2000, Eric Brewer stood at a symposium and proposed something that would become one of the most cited principles in distributed systems engineering. The CAP Theorem. Any distributed data store, he argued, can only guarant
The story everyone's missing while they argue about AI. While the Indian startup ecosystem has been consumed by funding cycles, AI hype, and the occasional unicorn story — something quieter and arguably more consequential has been happening. Global Capability Centres have been expanding across India at a pace that is r
The biggest unsolved problem in AI product design isn't hallucination. It's miscalibrated trust. I want to tell you about two users of the same AI platform. Same product. Same features. Same underlying model. Completely different relationships with what the AI tells them. The first user — a senior finance professional
AI didn't make technical skills irrelevant. It made the half-life of any specific skill shorter than it has ever been.
This isn't a compliance checkbox. It's a product architecture conversation.
We've spent a decade automating how we hire. We've barely started on what we do after.
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