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Why Data Tech is Driving Income Disparity via Vinod Khosla

Coming across this piece, The Next Technology Revolution Will Drive Abundance And Income Disparity by Vinod Khosla, I thought it adds a bit to the discussion in our blog post, This Time is Different: How Big Data Has Left the Middle Class Behind.  Khosla is a key figure in the history of tech, a co-founder of Sun Microsystems and a venture capitalist who invests in many "big data" companies.

Even as he sees great economic gains from new technology, he worries that machine learning may become so effective that it may change the work environment so dramatically that income disparity explodes even more.  Where This Time is Different emphasized the disparity in knowledge between consumers/workers and corporations and a key problem, Kholsa highlights why the ongoing Luddite fear of machines replacing humans may finally be coming true with this round of tech.  

As Khosla notes "In past economic history, each technology revolution—while replacing some jobs—has created more new types of job opportunities and productivity improvements, but this time could be different." Instead of augmenting human capabilities, this technology may be so superior in intelligence and knowledge than humans that it will relegate them not to higher levels of work but to lower levels where they "command lower pay."

While such technology may generate new wants and needs to generate new kinds of jobs, Khlosa questions defenders of the prospect of infinite job creation like Steve Rattner and Marc Andreessen on the grounds that more training for people may not necessarily create a place for them in servicing those needs-- or at least not in a position of greater skill and compensation. He goes so far as to quote Karl Marx on the idea that extrapolating the past is often a fallacy:  “when the train of history hits a curve, the intellectuals fall off.”

He notes that he was himself an advocate of some degree of income disparity where incentives for education would give everyone an opportunity for social mobility.  But if machine learning takes all the "best jobs" requiring the most skill, "an avenue of personal growth through education that previously has always been open for labor advancement may be closed.":

It seems likely that the top 10 to 20-percent of any profession, be they computer programmers, civil engineers, musicians, athletes or artists, will continue to do well. What happens to the bottom 20-percent or even 80-percent, if that is the delineation? Will the bottom 80-percent be able to compete effectively against computer systems that are superior to human intelligence?

The outcome could be (as recent growth as delivered in the US) a situation where overall wealth increases but the incomes of the median family sees none of it. 

Being a free market capitalist at heart, Khosla shies away from the obvious solution of greater economic redistribution of that wealth being generated by technology, but he offers little as an alternative to his bleak assessment of exploding inequality.  But his essay does reflect a growing admission even among elite sectors that while new technology may be generating great wealth, older nostrums of a rising tide lifting all boats are threadbare and increasingly irrelevant.

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