Artificial Intelligence and the Future of Work (2025) / Chapter Skim
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Summary I nterest in how advances in artificial intelligence (AI) will affect workers has been grow ing in recent years, especially with the rapid increase in capabilities and adoption of large language model (LLM)
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, poten tial government regulation, and decisions by companies to limit access to AI capabilities in light of these and other challenges, including privacy concerns. Other areas of AI -- including speech recognition; computer vision and other forms of computer perception; the application of machine learning to large, structured data sets; autonomous vehicles and robotics -- are experiencing slower but continued techni cal progress.
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Of course, many other tasks are not suitable for AI, at least in its current form. Considering the set of tasks potentially affected and the factors on which productivity effects depend, AI has the potential to increase aggregate productivity growth substantially for the broader economy in the coming decade.
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The future impact of AI on the demand for expertise is uncertain, but three plau sible scenarios emerge. First, AI could accelerate occupational polarization, automating more nonroutine tasks and increasing the demand for elite expertise while displacing middle-skill workers.
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AI 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 pre sentations and marketing materials, including charts, slides, and illustrations; and helping to manage complex systems such as computer networks and perhaps air traffic control systems. In such cases, AI is likely to substitute for human expertise, eroding the value of such expertise.
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• 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 work force and the degree to which governments subsidize the cost to workers of that retraining.
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Although there is not yet proof that these new LLM-driven methods lead to better student learning outcomes, they exemplify the explosion of creative new work going into designing and experiment ing with this new generation of AI teaching tools. Although the impacts of AI on the labor market remain uncertain, AI is likely to shift the demand significantly for different types of worker expertise and to result in a large increase in demand for continuing education and retraining programs to help workers acquire the expertise needed to adapt to the changing jobs environment.
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From page 10... ...
Another is to modernize measures of productivity growth that were originally devised for goods-producing sectors such as manufacturing, to cover diverse productivity impacts of software tools. Yet another is to manage the different data schemas and formats used by different data sources.
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