Erik Brynjolfsson
In plain wordsAn American economist who studies how digital technology and AI change productivity and jobs, and who warns against AI built to replace people rather than to augment them.
American economist, director of the Stanford Digital Economy Lab since 2020 and earlier a professor at the MIT Sloan School of Management. His 1993 review of the information-technology productivity paradoxproductivity paradox, n.: The puzzle that a powerful new technology, like computers in the 1980s or AI today, can be everywhere without showing up as faster growth in how much workers produce. — computers' failure to show up in productivity statistics — helped popularize the term, and with Daniel Rock and Chad Syverson he later argued that general-purpose technologies such as AI produce a productivity J-curve, depressing measured productivity before raising it. 'Race Against the Machine' (2011) and 'The Second Machine Age' (2014), both with Andrew McAfee, brought debates about automationautomation, n.: Using machines or software to do work that people would otherwise do, from factory robots to programs that sort email or write reports. and technological unemploymenttechnological unemployment, n.: Joblessness caused by machines or software taking over work that people used to be paid for, faster than new kinds of work appear for them. to wide audiences, and with Tom Mitchell and Daniel Rock he scored occupations' suitability for machine learning, an early measure of task exposuretask exposure, n.: A measure of how much of a job's work AI could, in principle, do or speed up. High exposure means many of the job's tasks are within AI's reach, not that the job will vanish.. A field study with Danielle Li and Lindsey Raymond (2023) found that an AI assistant raised customer-support agents' productivity by about 14 percent, mostly among novices. His essay on the Turing trapTuring trap, n.: The risk that by building AI mainly to imitate and replace people, rather than to extend what people can do, society ends up with less widely shared prosperity and weaker bargaining power for workers. (2022) argued that aiming AI at imitating rather than augmentingaugmentation, n.: Using AI to help people do their work better or faster while they stay in charge, instead of having the AI do the job on its own. people concentrates wealth and power.
Related
- productivity paradoxproductivity paradox, n.: The puzzle that a powerful new technology, like computers in the 1980s or AI today, can be everywhere without showing up as faster growth in how much workers produce.
- Turing trapTuring trap, n.: The risk that by building AI mainly to imitate and replace people, rather than to extend what people can do, society ends up with less widely shared prosperity and weaker bargaining power for workers.
- task exposuretask exposure, n.: A measure of how much of a job's work AI could, in principle, do or speed up. High exposure means many of the job's tasks are within AI's reach, not that the job will vanish.
- technological unemploymenttechnological unemployment, n.: Joblessness caused by machines or software taking over work that people used to be paid for, faster than new kinds of work appear for them.
- augmentationaugmentation, n.: Using AI to help people do their work better or faster while they stay in charge, instead of having the AI do the job on its own.
- automationautomation, n.: Using machines or software to do work that people would otherwise do, from factory robots to programs that sort email or write reports.
Sources
- Brynjolfsson, E. & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton.
- Brynjolfsson, E. (2022). The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence. Dædalus 151(2): 272–287.
- Brynjolfsson, E., Li, D. & Raymond, L. (2023). Generative AI at Work. NBER Working Paper 31161.
Mentioned in
Tom MitchellTom Mitchell, prop. n.: An American computer scientist who wrote a classic machine-learning textbook with the field's most quoted definition of learning, and founded the first university department devoted to machine learning.