A recent report highlights a notable use of artificial intelligence (A.I.) designed to support workers, specifically through a tool called the Electrician’s Assistant (EA) developed by Schneider Electric, a multinational firm hailing from France. This innovative assistant empowers electricians by allowing them to input data and images when they encounter difficult problems. The A.I. then analyzes the information, providing iterative diagnoses and tailored recommendations for troubleshooting. Additionally, EA facilitates the filing of maintenance reports, reportedly cutting the time required for this task by half. The report suggests that similar tools could potentially benefit various trades and professions, including plumbing, construction, and healthcare.
While this is a positive development, the report questions how widely such initiatives can be applied. For every beneficial A.I. example like the EA, there are instances where technology is seen as a reason for workforce reductions. Recently, Block, a financial services platform, announced plans to lay off 4,000 employees, asserting that their roles could be replaced by A.I. Furthermore, even companies that have integrated A.I. without substantial job cuts often do so in ways that surveil and pressure employees. Amazon, for instance, employs an Associate Development and Performance Tracker in its warehouses and uses constant video monitoring in its delivery vehicles. Similarly, Burger King is experimenting with A.I.-powered headsets to monitor customer service interactions for politeness.
Economists from M.I.T. stress the significant obstacles that lie ahead. According to Acemoglu, one of the economists involved, major corporations are not investing adequately in developing A.I. tools that benefit workers. To shift this trend, the researchers propose various policy changes, including adjustments to tax regulations, enhancing competition within the A.I. sector, and granting workers greater ownership stakes in A.I. advancements. A key recommendation is for the government to leverage its financial clout—both as a funder of research and a buyer of technological systems—to encourage the creation of A.I. that empowers the workforce. In sectors like healthcare and education, which represent about 25% of the nation’s GDP, government procurement could be a powerful tool to advocate for A.I. solutions that enhance worker capabilities. Autor emphasized this potential, noting that A.I. could assist nurses with complex tasks and enable teachers to provide personalized support to students. He mentioned the responsibility of taxpayers, stating, “We pay for this stuff, we use it, the welfare of our children and grandchildren depends on it.”
The report also discusses how the tax system could be leveraged to influence A.I. developers and users. Businesses often face a choice between investing in automation—like chatbots—or hiring and retraining staff. The current tax structure, favoring capital investment over labor, incentivizes the former. Autor suggested adjusting the tax code to raise capital taxes while lowering taxes on labor, thereby creating a more equitable structure. Another, albeit more radical approach, could involve taxing consumption instead of labor.
Of particular interest in the report is the section addressing “Discouraging expertise theft.” It outlines the current practices of A.I. companies that utilize online content—from websites and social media to newspapers and blogs—without compensating the creators, treating this material as training data. This practice is likened to historical land enclosure, where landlords benefitted while small farmers lost their livelihoods. Autor warned that this trend leads to the appropriation of creative work and a significant reallocation of property rights. Furthermore, when companies utilize employee insights to develop A.I. models, it raises ethical concerns, as few workers would willingly contribute their expertise to train systems that could ultimately replace them.