A few months ago, I was waiting for the subway with a friend, a professional editor, who had never used a large language model (LLM). Standing on the platform, she told me about an article she'd been working on. ChatGPT had come out six weeks earlier, and I input her summary into it on my phone and showed her the result. I'd been following OpenAI's transformer-driven models since 2019 and had forgotten the effect they can have on first exposure. My friend couldn't take her eyes off the little gray box as the article came out, line by line.

The middle and working classes have seen a steady decline in their fortunes. Sending jobs to foreign countries, the hollowing out of the manufacturing sector, pivoting toward a service economy and the weakening of unions have been blamed for the challenges faced by a majority of Americans. According to a new academic research study, automation technology has been the primary driver in U.S. income inequality over the past 40 years. The report, published by the National Bureau of Economic Research, claims that 50% to 70% of changes in U.S. wages, since 1980, can be attributed to wage declines among blue-collar workers who were replaced or degraded by automation. Artificial intelligence, robotics and new sophisticated technologies have caused a wide chasm in wealth and income inequality.

For one of the most distinguished critics of automation, MIT economist Daron Acemoglu has been, ironically, cranking out research on the subject lately like he's a machine. He and his co-author Pascual Restrepo have produced so many studies on the subject that he couldn't tell us how many they've done. "I've lost count," he says. Their conveyer belt of research has been spitting out some startling facts. They find, for instance, that each new industrial robot killed, on average, 3.3 jobs in America between 1993 and 2007.

Automation fears distract from the real problem: too few blue-collar workers. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Following the Great Recession, anxiety intensified over the prospect of automation causing permanent, widespread unemployment. Feeding on public alarm, a large number of studies assessed the likely impact of future automation on jobs. Although some touted the potential for job creation, others predicted catastrophic job loss. Today, after more than a decade of continuous U.S. economic expansion, the fear of automation remains entrenched in the country's psyche, dominating public discussions and political debates.

A blue-collar worker is a working class person who performs manual labor. Blue-collar work may involve skilled or unskilled manufacturing, mining, sanitation, custodial work, textile manufacturing, power plant operations, farming, commercial fishing, landscaping, pest control, food processing, oil field work, waste disposal, recycling, electrical, plumbing, construction, mechanic, maintenance, warehousing, shipping, technical installation, and many other types of physical work. Blue-collar work often involves something being physically built or maintained. In contrast, the white-collar worker typically performs work in an office environment and may involve sitting at a computer or desk. A third type of work is a service worker (pink collar) whose labor is related to customer interaction, entertainment, sales or other service-oriented work.

So the robots are coming for our jobs, are they? Goes back to Elizabeth I and the stocking frame, if my memory serves me right. Machines have been taking our jobs forever. But economists, despite their reputation as practitioners of the "dismal science", have always been upbeat about that. Sure, machines destroy jobs, they say.

Artificial intelligence is emerging as the driving force behind many advancements in technology, even though the industry has merely scratched the surface of what may be possible. But how deeply AI penetrates different market segments and technologies, and how quickly it pushes into the mainstream, depend on a variety of issues that still must be resolved. In addition to a plethora of technical issues, there needs to be progress in sanitizing data sets, resolving political, legal and ethical issues, and instilling trust in machines. None of these challenges is insurmountable, but a failure to deal with any of them will could delay the adoption of AI and slow or prevent it from reaching its full potential. Data cleaning AI applications start with large data sets. If the data is bad, the application is bad.

A new national survey commissioned by Northeastern University and conducted by Gallup finds most U.S. adults have an overall positive view of artificial intelligence, but believe they are ill-prepared to deal with AI's expected impact on the global digital economy. The survey comes on the heels of numerous international studies forecasting significant job loss resulting from AI. Overall, 22 percent of Americans with a bachelor's degree or higher level of education say their college or university studies prepared them well or very well to work with AI. Moreover, only 18 percent are extremely confident they could secure the education needed to obtain a comparable job should they lose their current position to advances in new technology. "The answer to greater artificial intelligence is greater human intelligence," said Northeastern President Joseph E. Aoun. "The AI revolution is an opportunity for us to reimagine higher education--to transform both what and how we teach. If colleges and universities can adapt and modernize, we can ensure that tomorrow's learners will be robot-proof."

Americans don't fear artificial intelligence as much as is commonly believed, a new study by Gallup and Northeastern University has found. Officials at Northeastern say that it shows higher education should be more involved in training people for the artificial intelligence world. In a survey of 3,297 adults, about three-quarters said artificial intelligence has and will continue to have a fundamental, but also positive, effect on their lives. Among blue-collar workers, that number dipped to 68 percent. But nearly three-quarters of participants (and 82 percent of blue-collar workers) admitted the revolution will take more jobs than it creates.