Expertise In The Era Of Artificial Intelligence
Excerpted from
How AI could Help Rebuild the Middle Class
in Noema by David Autor
Just as the Industrial Revolution technologies
eroded the value of Artisanal Expertise,
Advanced Computing eroded
the value of Mass Expertise.
Now like the Industrial and Computer
Revolutions before it,
the Artificial Intelligence Revolution
marks an inflection point in the
economic value of Human Expertise.
To appreciate why, consider that pre-AI computing’s core capability was execution of routine, procedural tasks -with little error and cost. While non-routine tasks, requiring tacit knowledge, its Achilles’ heel.
Artificial Intelligence’s capabilities are precisely the inverse.
If a traditional computer program
is akin to a classical performer
playing only the notes on the sheet music,
AI is more like a jazz musician.
Like a human expert, AI can weave formal knowledge (rules) with acquired experience to make —or support— one-off, high-stakes decisions.
AI’s capacity to depart from script,
to improvise based on training and experience,
enables it to engage in Expert Judgment
—a capability that, until now, has fallen
within the province of Elite Experts.
Though only in its infancy,
this is a Superpower.
As AI’s facility in expert judgment becomes more reliable, incisive, and accessible in the years ahead its primary role will be to advise, coach and alert decision-makers as they need, bleeding more and more into the edges of our everyday lives.
When your email application proposes to complete your sentence, your smartwatch asks if you’ve taken a fall or your car nudges your steering wheel to re-center your vehicle in a lane, AI is supplying Expert Judgment to interpret your intentions and guide your actions.
Unless you’re sleeping at the wheel of your Tesla, the stakes of most of these decisions are inconsequential at present, but they will rise as AI advances and takes on Higher-Value Assignments in Our Lives.
What does this epochal advance in machine capability imply for
the future of human expertise?
Despite its novelty, I believe
that the implications of AI have
a relevant parallel in economic history,
but the parallel runs counter to the present.
Recall that the advent of pre-AI computing made the expert judgment of professional decision-makers more consequential and more valuable by speeding the task of acquiring and organizing information. Simultaneously, computerization devalued and displaced the procedural expertise that was the stock-in-trade of many middle-skill workers.
But imagine a technology that could invert this process: what would it look like?
It would support and supplement judgment, thus enabling a larger set of non-elite workers to engage in high-stakes decision-making. It would simultaneously temper the monopoly power that doctors hold over medical care, lawyers over document production, software engineers over computer code, professors over undergraduate education, etc.
Artificial Intelligence
is this Inversion Technology.
By providing decision support in the form of real-time 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.
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.
Most people understand that mass production lowered the cost of consumer goods. The contemporary challenge is the high and rising price of essential services like healthcare, higher education and law, that are monopolized by guilds of highly educated experts.
Federal Reserve Bank economists Emily Dohrman and Bruce Fallick estimate that over the last four decades, the prices of healthcare and education rose by around 200 and 600%, respectively, relative to U.S. household incomes. Contributing to these increases is the escalating cost of employing elite decision-makers. Such gains are arguably justified: expertise commands a substantial premium when it is both necessary and scarce.
AI has the potential to empower more workers to do expert work,
reducing scarcity, and therefore bringing costs down.
Example: Nurse Practitioners
To make this argument concrete, consider an example that is not from the AI realm: the job of Nurse Practitioner (NP). NPs are Registered Nurses (RNs) who hold an additional master’s degree that certifies them to administer and interpret diagnostic tests, assess and diagnose patients, and prescribe medications —services that were once exclusively within the domain of physicians.
Between 2011 and 2022, NP employment nearly tripled to approximately 224,000, with employment projected to grow by around 40% over the next decade, well above the national average. In 2022, the median Nurse Practitioner earned an annual salary of $125,900.
NPs are elite decision-makers. Their work combines procedural expertise with expert judgment so they can confront one-off patient cases where the stakes for judicious decision-making are extraordinarily high.
What makes the NP occupation relevant here is that it offers an uncommonly large-scale case where high-stakes professional tasks —diagnosing, treating and prescribing— have been reallocated (or co-assigned) from the most elite professional workers (MDs) to another set of professionals (NPs) with somewhat less elite -though still substantial- formal expertise and training.
What made this sharing of elite decision-making rights possible?
The primary answer is institutional. In the early 1960s, a group of nurses and doctors recognized a growing shortage of primary care physicians, perceived that the skills of registered nurses were underused and pioneered a new medical occupation to address both shortfalls.
This required launching new training programs, developing a certification regime and winning a change in the scope of medical practice regulations with the physicians’ primary lobbying arm, the American Medical Association.
A complementary answer to this origin story is that information technology, combined with improved training, facilitated this new division of labor.
To put it more simply:
electronic medical records and
improved communication tools
enabled NPs to make better decisions.
Now more integrated AI assists, like ChatGPT, are scaffolding progress by helping healthcare marketers, grant writers, consultants, and managers accomplish research/writing efforts in 40% of the time it would take them without it.
ChatGPT did not eliminate the role of expertise, but it does enable the most capable to write faster and the less capable to write both faster and better —so the productivity gap between adequate and excellent writers shrank.
Other studies indicate AI tools supplemented expertise rather than displaced experts. This occurred through a combination of automation and augmentation. The benefit of automation was paid in time savings. AI wrote the first draft of computer code, advertising copy and customer support responses. The benefit of augmentation accrued in quality.
Using AI, less skilled workers
produced work closer in quality to that
of their more experienced and skilled peers.
And quality improved not simply because workers were asleep at the wheel while AI did the driving. Workers were called upon to apply their expertise and judgment to produce the final product of code, text or customer support, while also harnessing AI’s suggestions.
From contract law to calculus instruction
to catheterization, AI could potentially
enable a larger set of workers
to perform high-stakes expert tasks.
But what’s true about employment in a specific product or service has never been true of the economy writ large. When nearly 40% of U.S. workers were on farms, the fields of health and medical care, finance and insurance, and software and computing had barely germinated.
The majority of contemporary jobs are not remnants of historical occupations that have so far escaped automation. Instead, they are new job specialties that are inextricably linked to specific technological innovations; they demand novel expertise that was unavailable or unimagined in earlier eras.
There were no air traffic controllers, electricians or gene editors until supporting innovations gave rise to the need for these specialized skill sets. Nor is technology the full story. Many expert personal service occupations, such as vegan chefs, college admissions consultants and personal trainers, owe their livelihoods not to specific innovations but to rising incomes, fluctuating fashions and shifting economic incentives. Innovation contributes to this by expanding the economic pie, thus allowing societies to call for richer slices.
Facing decades ahead of stagnating population growth and a rising share of citizens who are long past retirement, the challenge for the U.S. and the entire industrialized world is not a shortfall of work but a shortfall of workers.
In the rapidly aging country of Japan, for example, the Financial Times reports that “Japanese retailers have shortened operating hours, installed avatars and hired foreign students to cope with the labour shortage.”
We must be clear in gleaning that rather
than catalyzing a new era of
global scale democratization,
the Internet Revolution actually
exacerbated a trend of rising inequality, as well.
It would serve the U.S. and other industrialized
countries well if AI-enabled more workers
to use their expertise more effectively.
AI poses a real risk to labor markets,
but not that of a technologically jobless future.
The risk is the devaluation of expertise.
The unique opportunity that AI offers humanity is to turn back this tide —to extend the relevance, reach and value of human expertise for a larger set of workers.
Not only could this dampen earnings inequality and lower the costs of key services like healthcare and education, but it could also help restore the quality, stature and agency that has been lost to too many workers and jobs.
This alternative path is not an inevitable or intrinsic consequence of AI development.
We should ask not what AI will do to us,
but what we want it to do for us.
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#AI #ArtificialIntelligence #FutureofWork #Upskilling
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