Artificial Intelligence and the Future of Work
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In 2022 Congress requested a study by the National Academies on the current and future impact of AI on the US workforce. The report, “Artificial Intelligence and the Future of Work,” was released in November of 2024. The three year study was conducted by a Committee of experts from universities and private sector institutions co-chaired by Stanford professor Erik Brynjolfsson and CMU professor Tom Mitchell.
“Today, the speed of technological progress is reshaping not just the tools but also the fabric of the workforce and societal structures,” said the report in its Introduction chapter. “AI has emerged as a general-purpose technology with sweeping implications that demand immediate attention and thoughtful analysis. AI stands out among general-purpose technologies owing to its core attribute — a focus on intelligence. This arguably makes it the most general of all general-purpose technologies.”
“The trajectories that Al-enabled futures might take can lead to outcomes of profound benefit or significant disruption. The goal of this report is thus twofold: to responsibly inform about the current state and capabilities of Al as they relate to the workforce and to offer insights that prepare us for the challenges ahead and opportunities that will arise. It also considers how Al is likely to augment human labor, reshape job markets, and influence workforce dynamics.”
The long (140 pages), comprehensive report covers a lot of ground in its seven chapters. Let me discuss some of its major points.
Key Findings
The report’s eleven key findings are listed in the Summary chapter and explained in greater detail throughout the report:
AI is a general-purpose technology that has recently undergone significant rapid progress. Still, there is a great deal of uncertainty about its future course, suggesting that wide error bands and a range of contingencies should be considered.
AI systems today remain imperfect in multiple ways. For example, LLMs can “hallucinate” incorrect answers to questions, exhibit biased behavior, and fail to reason correctly to reach conclusions from given facts.
Significant further advances in AI technology are highly likely, but experts do not agree on the exact details and timing of likely advances.
The substantial and ongoing improvements in AI’s capabilities, combined with its broad applicability to a large fraction of the cognitive tasks in the economy and its ability to spur complementary innovations, offer the promise of significant improvements in productivity.
As was the case with earlier general-purpose technologies, achieving the full benefits of AI will likely require complementary investments in new skills and new organizational processes and structures.
The labor market consequences of widespread AI deployment will depend both on the rate at which AI’s capabilities evolve and on demographic, social, institutional, and political forces that are not technologically determined.
AI can be used to improve worker outcomes or to displace workers. Too often an exclusive focus on worker displacement neglects two other potentially positive labor market consequences of AI — new forms of work that demand valuable new expertise and AI systems that work jointly with workers to enable them to use their expertise more effectively to accomplish a broader variety of valuable tasks, perhaps with less formal training.
History suggests that even if AI yields significantly higher worker productivity, the productivity gains might fall unevenly across the workforce and might not be reflected in broad based wage growth.
AI will have significant implications for education at all levels, from primary education, through college, through continuing education of the workforce. It will drive the demand for education in response to shifting job requirements, and the supply of education as AI provides opportunities to deliver education in new ways. It may also shift what is taught to the next generation to prepare them to take full advantage of future AI tools and advances.
Better measurement of how and when AI advancements affect the workforce is needed. To help workers adapt to a changing world, improving the ability to observe and communicate these changes — such as the impact of LLMs on knowledge work and robotics on physical work — as they occur is crucial
Responses to concerns that AI poses potentially serious risks in areas such as fairness, bias, privacy, safety, national security, and civil discourse will modulate the rate and extent of impact on the workforce. It will take deep technical knowledge and may require new institutional forms for governments to stay abreast of and address these issues, given the rapidly changing technology.
The Likely Evolution of AI
The current rapid rate of Al progress is likely to continue for some years, said the report, “owing to expected large commercial and government investments to develop bigger and better models, the availability of increasingly diverse and ever larger data sets to train Al systems, progress in open-source efforts to develop more shareable and portable models, and a burst of effort by both start-ups and mature corporations to apply this technology to a wide range of applications.”
At the same time, other forces will work to slow down the rate of advance, “such as the need to address shortcomings of this imperfect technology, the need for the technology to be socially acceptable and trusted by the public (e.g., to avoid implicit social biases or to avoid helping bad actors achieve harmful goals), potential government regulation, and decisions by companies to limit access to Al capabilities in light of these and other challenges, including privacy concerns.”
“There is great uncertainty regarding which specific Al capabilities will appear in the coming years and when. As a result, decision makers need to create policies that will be robust to a variety of possible future technology advances and timetables. Moreover, the capabilities developed and, more importantly, whether and how they are implemented will depend on society's collective choices.”
AI and the Workforce
Throughout the Industrial Revolution there were periodic panics about the impact of automation on jobs, going back to the so-called Luddites, — textile workers who in the 1810s smashed the new machines that were threatening their jobs. Automation anxieties continued to resurface in the 20th century, right along with advances in technology. In a 1930 essay, for example, English economist John Maynard Keynes wrote about the onset of a new disease which he named technological unemployment, — i.e., “unemployment due to our discovery of means of economising the use of labour outrunning the pace at which we can find new uses for labour.”
Automation fears have understandably accelerated as our increasingly smart machines are now being applied to activities requiring intelligence and cognitive capabilities that not long ago were viewed as the exclusive domain of humans.
Although there is widespread concern about the impacts of Al on jobs, there are a number of factors that ameliorate these concerns. First, — apart from a spike in unemployment due to the COVID-19 pandemic, — US unemployment rates have been quite low compared to historical levels. Second, population and growth rates in the US and other advanced economies have been declining and are expected to continue to do so. And third, the adoption of AI in the workplace is still in its early stages, making it difficult to estimate the longer term impact of AI on the future of work.
In addition, as explained by MIT economist David Autor in his 2015 paper, “The History and Future of Workplace Automation,” most jobs involve a number of tasks or processes. Some of these tasks are more amenable to automation, while others require judgement, social skills and other human capabilities. While automation does indeed substitute for labor, automation also complements labor, raising economic outputs by enabling workers to focus on those aspect of the job that most need their attention. AI will thus spur the emergence of new occupations by altering the share of tasks that will be done in collaboration between human workers and Al tools. And, it will likely transform the nature of existing occupations by eroding the value of old expertise while creating demand for new kinds of expertise.
“There is, however, substantial uncertainty about what types of new work will follow from the widespread use of AI, what skills it will require, what it will pay, how much of it there will be, and who will do it,” said the report. “But there is no question that AI will both strand some forms of human expertise and create demands for others. The following questions are thus key to assessing the impact of AI advances on jobs”:
What expertise will be substituted with or made obsolete by Al? “Al tools may soon equal or exceed human capabilities in a variety of tasks requiring elite expertise, such as digesting and summarizing large document collections; proofreading; writing certain business and legal documents; producing presentations and marketing materials, including charts, slides, and illustrations; and helping to manage complex systems such as computer networks and perhaps air traffic control systems.”
What expertise will be augmented or newly demanded as a result of Al adoption? “The answer to this is even more uncertain than the answer to the previous question. However, it appears likely that Al will be used in many (not all) cases to assist humans in performing tasks, thereby augmenting and complementing their expertise rather than substituting for it.”
How feasible will it be for workers to acquire newly valuable expertise? “Fortunately, this is a question over whose answer society has some control — one can choose whether to enrich the educational opportunities made available to the workforce and the degree to which governments subsidize the cost to workers of that retraining.”
How will the organization of work and employer power dynamics evolve? “The consequences of previous technological transitions for the welfare of workers and the strength of the middle class have depended not only on the nature and application of technologies but also on the frameworks and legal institutions that have shaped their design, adoption, and use and the distribution of economic surplus among owners, managers, and line workers.”
The Road Ahead
“It is impossible to predict exactly the nature of the coming changes in Al and all of their effects on the economy and society,” said the National Academies report in conclusion. “Accordingly, it makes sense to build in the ability for rapid data gathering and analysis to track these changes, and to build as flexible an approach as possible for reacting to the changes observed.” In practice, this means increased research on AI technologies and on its social and behavioral implications.
“It also means that rather than trying to predict any specific future path, society needs the flexibility to rapidly sense and respond to opportunities and challenges and to be prepared for a variety of scenarios and possibilities. Most importantly, as Al becomes more capable, policy makers, business leaders, Al researchers, employers, and workers all have an opportunity to shape the future of the workplace and workforce in ways that are consistent with societal values and goals.”
Irving Wladawsky-Berger is a Research Affiliate at MIT's Sloan School of Management and at Cybersecurity at MIT Sloan (CAMS) and Fellow of the Initiative on the Digital Economy, of MIT Connection Science, and of the Stanford Digital Economy Lab.