The Laptop I Won

In 2002, I got my first laptop. Won it in a sales contest. A prized possession.

One afternoon in Bandra, I left it in my Wagon R for a few minutes. (Correction: not my Wagon R. It belonged to my then-girlfriend. She had a driver. I had BEST buses, autos and sweaty local train compartments. You get the drift.)

When I came back, the laptop was gone. Just like that.

I marched into the nearest police station — chest puffed, rehearsing lines. The officer listened patiently, then sent the poor driver out. Ten minutes later, the fellow returned — shaken, weeping, hands folded like I was some village zamindar.

Turned out he wasn’t the thief. Just the fool. Somebody had pulled that old trick: drop a bundle of notes, distract, and whisk the real prize away. He hadn’t told me because he was ashamed.

The cop smiled. “He was answering my questions looking at you, not me. Trying to convince you of his innocence. That told me something was off. I won’t file a complaint — it would hurt my appraisal — but give me a day or two. I’ll get your laptop back.”

He wasn’t lying. The station knew the gangs, the distraction tricks, the pawn shops. Two days later, he called: come pick it up. There it was, lying among thirty others, like a sheepish schoolboy caught bunking class.

Years earlier, my boss — nephew of a top cop — introduced me to the hidden grammar of policing: the networks, the “khabris”, the paid informants. Cops often knew who ran the rackets. Some were even complicit — they claimed it helped control the flow and prevent chaos, that it kept violence and serious crime down, while the petty ones ticked along like the city’s humidity.

Inside companies, people show the same tacit skill. A brilliant Head of Risk could tell in minutes who was lying. Where we unsuccessfully threw money, data, and footage at a problem, he solved it by asking questions and spotting tiny inconsistencies. “How do you do that?” I asked. He shrugged: “I don’t know. I just know.”

And in sales, after years on the frontline, I could sense who would buy, who wouldn’t, who was wasting my time. My team demanded I explain. I tried. I couldn’t. It was experience distilled into instinct.

None of this is anti-tech. AI, ML, and Neural Networks are extraordinary — they surface patterns, uncover correlations we miss. But human signals exist outside algorithms: a furtive glance, an embarrassed gait, intuition honed by thousands of conversations.

Algorithms can read the lines. But only we can read — and misread — between them.

And maybe that’s just as well. Machines may never misread, but they’ll also never know the awkward dignity of a driver too ashamed to confess, or a cop who quietly bends the rules to set things right. That messy space — between what is said and what is meant, between trust and doubt — is where we stumble, where we sometimes get fooled, but also where we find loyalty, love, and the odd miracle of a recovered laptop.