This is fine, we don't need to panic about this. India has done this dance before.
The boring, low-paid entry job is almost always on our plate.
Let me explain what is happening, then why I am not worried about it.
Robots can think now but cannot move well in the real world. To teach one to fold a towel or slice a mango, you need HD footage of thousands of hours of humans doing it.
A company called Objectways hires people in Karur and Chennai to wear head cameras and record exactly that.
The pay is ₹250, about $2.6, for an hour of video.
Their clients are Fortune 500 firms and they run on Amazon's machine learning platform.
It looks like exploitation in the video, but go back to the late 1990s. I
ndia's first big tech wave was Y2K work. Thousands of engineers fixing a boring date bug in old software, line by line, for low wages. It was grunt work nobody else wanted.
That grunt work built TCS, Infosys and Wipro. It trained a generation of engineers, earned the trust of global clients, and created the muscle memory of delivering for foreign companies at scale.
The boring bug-fixing was the foundation the entire $250 billion IT industry stands on.
Then the call centres came in the 2000s. Same sneer. "India is just the world's back office answering phones."
Those same call centres slowly became KPO, then analytics, then full engineering centres.
Look where that ended up. India now has over 2,100 Global Capability Centres employing more than 2.3 million people, with revenues nearing $100 billion.
These started as cost-arbitrage back offices. They are now R&D and engineering hubs. Over 1,200 of them now do AI and ML work, making India the second largest employer of enterprise AI talent in the world.
These jobs become the doorway.
GCCs today pay a 12 to 20% premium over old IT firms and actively hire freshers.
Every tech wave enters India through the least glamorous door.
Data entry became analytics. Testing became engineering. Annotation, labelling images for self-driving cars five years ago, became the ML-ops and model-evaluation work that pays well today.
So the head-camera is the entry point of the next wave, which is physical AI and robotics.
Think about what the worker who records 90 videos a day now understands.
They understand what egocentric data is, what clean capture looks like, why lighting and angles matter, what the robot needs. All of this is domain knowledge.
The next step up is quality control, then annotation, then dataset design, then training pipelines. Each step pays more.
Also the economics for the worker right now are simply good.
"Who else will give you 250 rupees an hour just for doing housework?" one of them asked.
For a housewife in Chennai or a fresher in a small town, flexible work-from-home money that did not exist two years ago is not a problem to solve.
But, India should not stay only the supplier of raw footage the way it once stayed only the supplier of cheap code.
The footage of an Indian kitchen is a dataset, and datasets are assets. The goal is to move up the ladder faster this time, so the value compounds here and feeds Indian robotics too, not just foreign clients.
But you do not climb a ladder by refusing to step on the first step.
So, yes record the mangoes, wear the camera. Take the ₹250. India has turned worse starting points than this into million-job industries three times in thirty years.
This is wave four, and the story is the same. :)
re-release is all good in the 2-3 hour at theatre and once you step outside boom back to real and routine life. rajni gets older and older and us too.
#Padaiyapaa
@Lazy_Pay , I am being bombarded with due emails for loans I NEVER took! It seems my email is linked to someone else's account. This spam is unacceptable, and I've had enough. Fix this immediately or expect further action.This harassment has to stop!
#PrivacyViolation#LazyPayScam
The best traits a young man can have.
- Curiosity
- Ambition
- Courage
- Patience
- Discipline
- Resilience
- Determination
If you have all these, your chances of failure in life are 0.
It’s smart to take help of a pessimist to find your blind spots in an idea you feel very confident about.
That’s the only good use of them I have found so far.