Optimistic paper by Prof David Autor on how we can use AI to create jobs:
While the utopian vision of the current Information Age was that computerization would flatten economic hierarchies by democratizing information, the opposite has occurred. Information, it turns out, is merely an input into a more consequential economic function, decision-making, which is the province of elite experts.
The unique opportunity that AI offers to the labor market is to extend the relevance, reach, and value of human expertise. Because of AI’s capacity to weave information and rules with acquired experience to support decision-making, it can be applied to enable a larger set of workers possessing complementary knowledge to perform some of the higher-stakes decision-making tasks that are currently arrogated to elite experts, e.g., medical care to doctors, document production to lawyers, software coding to computer engineers, and undergraduate education to professors.
My thesis is not a forecast but an argument about what is possible: AI, if used well, can assist with restoring the middle-skill, middle-class heart of the US labor market that has been hollowed out by automation and globalization.
Autor discusses how diferent technologies impacted diffeent levels of employment.
He starts with 18th century where artisans were valued:
Most “experts” of our era would be at a loss if teleported back to the 18th century. Prior to the Industrial Revolution, goods were handmade by skilled artisans: wagon wheels by wheelwrights; clothing by tailors; shoes by cobblers; timepieces by clockmakers; firearms by blacksmiths. Artisans spent years acquiring at least two broad forms of expertise: procedural expertise, meaning following highly practiced steps to produce an outcome; and expert judgment, meaning adapting those procedures to variable instances.
Mass production led to decline in demand for artisans and rise in mass expertise:
Although artisanal expertise was revered, its value was ultimately decimated by the rise of mass production in the 18th and 19th centuries (Hounshell, 1984). Mass production meant breaking the complex work of artisans into discreet, self-contained and often quite simple steps that could be carried out mechanistically by a team of production workers, aided by machinery and overseen by managers with higher education levels. Mass production was vastly more productive than artisanal work, but conditions for rank-andfile workers were typically hazardous and grueling, requiring no specialized expertise beyond a willingness to labor under punishing conditions for extremely low pay.
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As the tools, processes and products of modern industry gained sophistication, demand for a new form of worker expertise — “mass expertise” — burgeoned (Goldin and Katz, 1998; Buyst et al., 2018). Workers operating and maintaining complex equipment required training and experience in machining, fitting, welding, processing chemicals, handling textiles, dyeing and calibrating precision instruments, etc. Away from the factory floor, telephone operators, typists, bookkeepers and inventory clerks, served as information conduits — the information technology of their era.
Computers led to another wave of change with rise of people who could do routine tasks:
Stemming from the innovations pioneered during World War II, the Computer Era (AKA the Information Age) ultimately extinguished much of the demand for mass expertise that the Industrial Revolution had fostered. The unique power of the digital computer, relative to all technologies that preceded it, was its ability to cheaply, reliably and rapidly execute cognitive and manual tasks encoded in explicit, deterministic rules, i.e., what economists called “routine tasks” and what software engineers call programs.
AI creates the next wave which tries to figure non-routine tasks:
Like the Industrial and Computer revolutions before it, Artificial Intelligence marks an inflection point in the economic value of human expertise. To appreciate why, consider what distinguishes AI from the computing era that we’re now leaving behind. Pre-AI, computing’s core capability was its faultless and nearly costless execution of routine, procedural tasks. Its Achilles’ heel was its inability to master non-routine tasks requiring tacit knowledge. Artificial Intelligence’s capabilities are precisely the inverse.
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Artificial Intelligence is this inversion technology. By providing decision support in the form of realtime guidance and guardrails, AI could enable a larger set of workers possessing complementary knowledge to perform some of the higher-stakes decision-making tasks currently arrogated to elite experts like doctors, lawyers, coders and educators.4 This would improve the quality of jobs for workers without college degrees, moderate earnings inequality, and — akin to what the Industrial Revolution did for consumer goods — lower the cost of key services such as healthcare, education and legal expertise.
Vey interestingly put..
Mostly Economics 
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