How leaders think about jobs needs to change. I am a trustee on the board of an NGO called Anatarang. We work with disadvantaged youth in the 18 to 24 years age group to help them get jobs connected to their passions & break the intergenerational cycle of poverty.
As a leader, I believe that we must embrace the new opportunities that exponential technologies will throw up. It is a fact that disruptions are harder for the underprivileged, “The vulnerable will be the most vulnerable”.
And yet these new technologies will completely reinvent jobs across function & hierarchy.
More new jobs will be created by AI, than what it takes away!
Rethinking education & jobs is going to be critical for India.“By the year 2050, it’s estimated that India’s workforce age population will be comparable in size to that of China’s today — over 800 million people strong.
However, given that this is at least 30 years in the future, it raises all kinds of questions around the economic relevance of a working-age population in a landscape potentially dominated by technologies such as artificial intelligence and automation”
https://www.visualcapitalist.com/populations-china-india-diverging-demographics/
AI might create entirely new work for humans. Some such new work is easy to predict: Today’s legions of machine learning engineers and research scientists—not to mention AI solutions architects, sales engineers, and consultants— will undoubtedly proliferate. This growth, meanwhile, may be exceeded by the growth of a very different group of workers—those who manually label data to train AI algorithms.
“Ghost work” is anthropologist Mary L. Gray’s term for the invisible labor that powers our technology platforms.
“training most of today’s state-of-the-art AI models involves the manual cleaning and labeling of large datasets. This process is laborious, expensive, and among the biggest barriers to more widespread adoption of AI.To maintain accuracy, new training data needs to be continually captured, labeled, and fed back into the system. Although techniques like drift detection and active learning can reduce the burden, anecdotal data shows that many companies spend up to 10-15% of revenue on this process”(source: Andreesen Horowitz).
Imagine if you as a corporate leader decided to ask your company to use a % of their CSR budget to create data sets for Machine learning.
India & China are at opposite ends of the demographic shift India's working-age population will be almost 1 billion adults within the next decade, while for China these numbers will drop! So while China is far ahead of India in the AI game, what if we looked at creating Data moats! Data network effects occur when your product, generally powered by machine learning, becomes smarter as it gets more data from your users.
“Many services delivered by companies like Amazon, Google, Microsoft, and Uber can only function smoothly thanks to the judgment and experience of a vast, invisible human labor force. These people doing "ghost work" make the internet seem smart. They perform high-tech piecework: flagging X-rated content, proofreading, designing engine parts, and much more. An estimated 8 percent of Americans have worked at least once in this “ghost economy,” and that number is growing. They usually earn less than legal minimums for traditional work, they have no health benefits, and they can be fired at any time for any reason, or none”.
Companies spent almost $1 billion on CSR in India in 2018! What if companies used a part of their CSR budgets to create unique labeled training datasets for becoming AI-ready! The true competitive differentiator in the future is not AI but how you curate data for AI! This might also mean new jobs at the entry-level.