The Next Technology Revolution Will Drive Abundance And Income Disparity

There have been and will continue to be multiple big technology revolutions, but the most impactful on human society may be the one that finally builds systems with judgment and decision-making capability more sophisticated and nuanced than trained human judgment.

Machine learning, sometimes called big data or artificial intelligence, is making rapid progress in complex decision-making (for instance: driving a car was thought to be too difficult for computers even five years ago). Without speculating on what is probable, it is at least possible that such systems may even be better at creativity, emotion and empathy than human beings (for instance: writing the best music, love story or creative fiction). At the very least these systems may be able to handle much more data to which we now have access and use it to make better judgments than humans with their supposed instinct, gut, holistic and integrative decision capability.

Although any one software program may not do everything a human brain can do, specialized programs will likely make decisions and predictions in their domain better than most trained humans. Many, if not most, domains will be well covered by such programs. Many problems in our work environments aren’t ones the human brain evolved to solve for in the African savannah. To achieve these goals, a machine learning system does not need to exactly replicate the brain or even use brain like techniques.

While the future is promising and this technology revolution may result in dramatically increasing productivity and abundance, the process of getting there raises all sorts of questions about the changing nature of work and the likely increase in income disparity. With less need for human labor and judgment, labor will be devalued relative to capital and even more so relative to ideas and machine learning technology. In an era of abundance and increasing income disparity, we may need a version of capitalism that is focused on more than just efficient production and also places greater prioritization on the less desirable side effects of capitalism.

Let’s look at the scale of change that the new machine learning and data revolution may bring and why it potentially could be different than prior technology revolutions like mobile phones, accessible computing and automobiles. Just in the Khosla Ventures portfolio alone, entrepreneurs already are trying to use machine learning technologies to replace human judgment in many areas including farm workers, warehouse workers, hamburger flippers, legal researchers, financial investment intermediaries, some areas of a cardiologist’s functions, ear-nose-throat (ENT) specialists, psychiatrists and many others. Efficiency in the business world generally means reducing costs, which results in using fewer well-paid but highly skilled minds and the technology they develop or capital to replace lower paid and less skilled workers.

Our portfolio represents only a tiny fraction of the efforts around machine learning. Consider replacing taxi drivers (Google’s driverless cars), IT administrators (Grok on Amazon Web Services) and even hedge fund traders. Renaissance Capital, one of the top performing hedge funds that has consistently beat the Standard & Poor’s 500-stock index, does not hire traditional Wall Street talent like analysts but instead uses machine intelligence. “The firm’s scientists tap decades of diverse data in Renaissance’s vast computer banks to assess statistical probabilities for the direction of securities prices in any given market.” Another machine learning system even performs difficult jobs like scheduling night workers for the Hong Kong subway system, the busiest and most efficient in the world. These are not just traditional low skilled jobs susceptible to replacement.


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. Economic theory is largely based on an extrapolation of the past rather than causality, but if basic drivers of job creation change then outcomes may be different. Historically, technology augmented and amplified human capability, which increased the productivity of human labor. Education was one method for humans to leverage technology as it evolved and improved. However, if machine learning technologies become superior in both intelligence and the knowledge relevant to a particular job, human employees may be rendered unnecessary or in the very least, they will be in far less demand and command lower pay.

Machines with unlimited and rapidly expanding human-like capabilities may mean there will no longer be as much need to leverage human capabilities. In fact, there may be little for humans to augment or amplify even as productivity per human hour of labor increases dramatically all while far fewer people are needed for most tasks. This is not to say all human functions will be replaced but rather that many, and maybe even a majority, may not be needed.

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It is possible that machine learning technologies in the next 50 years will create a greater abundance of goods and services than we could imagine. Initially, machine intelligence will exceed human judgment in a few narrow areas and then, more broadly over time, will increase traditional measures of productivity and increase economic growth over where it might otherwise have been. In my view, capitalism is very good at promoting efficiency but now has moved to demand generation, making us want things we did not know we wanted. I suspect this trend will persist and the demand for goods and services will continue on an upward trajectory.

Many like Steve Rattner and Marc Andreessen have written on the subject of technology and proposed arguments ranging from Luddism and the “lump-of-labor” fallacy to economist Milton Friedman’s take that human wants and needs are infinite. I suspect they are right but that does not mean we will not see increasing income disparity with the next machine learning based technology revolution. Others like Erik Brynjolfsson are more contemplative but still miss the difference between past technology revolutions and machine learning technology.

The traditional view is that historically over time as jobs have been displaced, new ones have been created and to think otherwise is a Luddite fallacy. Steve Rattner argues that technology comes down to the concept of producing more with fewer workers or becoming more efficient (what economists call “productivity”). Without higher productivity, wages and standards of living cannot go up. He goes on to state that as technology has changed the nature of work—more specialized training is now required for many jobs—and consequently, it has contributed to a sharp rise in income inequality. We should be embracing technology not fearing it and that means educating and training Americans to perform more skilled jobs. He agrees that not every worker can be retrained, and so we must help those who aren’t suitable for new jobs with more robust social welfare programs, but he seems to treat it as a minor, not major problem.

What if machines, which may soon exceed the capability of human judgment, do most jobs better than humans even if people receive additional training? The magnitude of the problem of displaced workers and increasing income disparity especially in the face of abundance (increasing GDP) may become substantially larger. It is possible that this particular technology revolution does not allow for human augmentation and amplification by technology to a large enough degree and that education and retraining are not solutions at all, except for a very small percentage of the workforce. As Karl Marx said, “when the train of history hits a curve, the intellectuals fall off”.

Extrapolation of our past experiences, a favorite technique of economists, may not be a valid predictor of the future—the historical correlation may be broken by a new causality. Efforts at estimating the number of jobs that are susceptible to computerization underestimate how technology may evolve and make assumptions that seem very likely to be false, similar to past “truths” (like the waning correlation between productivity and income growth for labor). Even with this underestimate, researchers concluded that of the 702 job functions studied, 47-percent are at risk of being automated.


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