Experience Stern | Faculty & Research

In January 2015, Congress convened the inaugural meeting of its newly-formed Sharing Economy Caucus. Led by Representatives Jerome Nadler and Eric Swalwell, a group of lawmakers, tech CEOs, and a few fortunate academics like me gathered in Washington DC to debate the future of work. Faced with an explosion of platform entrepreneurship, freelancers, and other non-traditional work arrangements, the caucus discussed how to design a more inclusive social safety net within our existing system whose funding model was predicated on a single work arrangement, regular employment. A decade later, we have taken important strides forward. Many states, including Washington and California, have improved the portability of benefits, extending parts of the social safety net to platform workers via legislative action and ballot initiatives. There is growing evidence-based understanding of the advantages and limitations of alternative funding models and guaranteed income initiatives. 

Now, as we enter an era of work uncertainty catalyzed by the explosion of artificial intelligence (AI), two new challenges take center stage. As individual human capabilities are progressively embedded into generative AI systems, we must redefine the boundaries of intellectual autonomy to preserve people’s economic returns from their human capital investments, refashioning the law to answer a seemingly simple but profoundly important new question: What facets of “human capital” should be owned by humans? And as AI rapidly alters the mix of what humans and machines do, we must invest in high-quality national infrastructure for mid-career occupational transition with dignity.

A distinguishing characteristic of the generative AI technologies that have captivated our imaginations over the last two years is their ability to generate and create after being trained on what humans generated and created in the past. The machine learning breakthroughs underpinning these breathtaking innovations are thus inextricably intertwined with a sobering realization: a collection of human “works” is now no longer simply that human’s artistic or productive output, but the blueprint for an AI machine that can replicate parts of the creative process, talent, or human capital of an individual human creator. One can now easily train a generative AI system to write like your favorite author, compose music in the style of a specific musician, or generate new art that mirrors that of Picasso or Van Gogh. 

Read the full Aspen Institute article.
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Arun Sundararajan is Harold Price Professor of Entrepreneurship, Director, Fubon Center for Technology, Business and Innovation, Professor of Technology, Operations and Statistics and Undergraduate Faculty Advisor, Entrepreneurship