Hi, I'm Pranjal Desai. I have a degree in economics, but I don't call myself an economist. Yet.

Most economic writing is either too technical to reach anyone or too simple to be true. I try to sit in between.

I studied economics at NMIMS and spent my first few years learning what the discipline looks like outside a classroom — regulatory frameworks and compliance work at EY, fieldwork and survey design at BIAS, and then a year on my own with fifteen years of RBI data, asking who India's growth after 2014 actually reached. Short answer: in the states we call success stories, not everyone.

This year I've been closing the distance between economics and tech. Python and data analysis on Coursera, Claude 101, and a FinTech & AI certification at IIM Trichy — not because economics needs rescuing by software, but because most of the questions I find interesting now arrive as data problems first. The biggest of them: what happens to markets, jobs and incentives when machines can think? AGI changes everything the discipline studies — labour markets, productivity, inequality, monetary policy. Economics deserves a bigger seat at that table, and I'd like to help pull up a chair.

Most recently I was at Equilibrium Insights, a market research and consumer insights firm in Mumbai, where we built a SaaS platform that takes primary research — a process still largely held together by spreadsheets, phone calls and goodwill — and makes it methodical, on the client side and the respondent side both. I tested the tool and worked with the tech team on where it went next, including its interface and the socio-economic case we built around it. Alongside that I ran client projects end to end: research questions and discussion guides, coordinating recruiters and field agents, conducting the interactions, then analysing, drafting and presenting what we found.

What I took from all of it: consumer insight is an economic question, not only a reported one. Economics tends to turn up everywhere, once you start looking for it. The work has taught me to ask who a decision changes things for, what the data misses, and how to explain the answer without losing the people inside it.