Throughout the Industrial Revolution there were periodic panics about the impact of automation on jobs, going back to the Luddites, - textile workers who in the 1810s smashed the new machines that were threatening their jobs. But each time those fears arose in the past, technology advances ended up creating more jobs than they destroyed.
Automation fears have understandbly accelerated in recent years, as our increasingly smart machines have been applied to activities requiring intelligence and cognitive capabilities that not long ago were viewed as the exclusive domain of humans. Over the past decade, powerful AI systems have matched or surpassed human levels of performance in a number of tasks such as image and speech recognition, skin cancer classification, breast cancer detection, and highly complex games like Go. More recently, large language models (LLMs) and chatbots like ChatGPT are taking AI-based automation to a whole new level.
“OpenAI’s ChatGPT is the latest advance in a steady march of innovations that have offered the potential to transform many occupations and wipe out others, sometimes in tandem,” wrote journalists Lydia DePillis and Steve Lohr in “Tinkering With ChatGPT, Workers Wonder: Will This Take My Job?,” a recent NY Times article. “It is too early to tally the enabled and the endangered, or to gauge the overall impact on labor demand and productivity. But it seems clear that artificial intelligence will impinge on work in different ways than previous waves of technology.” In particular, AI is now “confronting white-collar professionals more directly than ever. It could make them more productive — or obsolete.”
“Artificial intelligence and machine learning have been operating in the background of many businesses for years, helping to evaluate large numbers of possible decisions and better align supply with demand, for example,” the article added. “ChatGPT, however, is the first to confront such a broad range of white-collar workers so directly, and to be so accessible that people could use it in their own jobs. And it is improving rapidly, with a new edition released this month.”
In the past few decades, the jobs most susceptible to automation were blue-collar occupations in manufacturing industries, and white-collar occupations like accounting and record keeping. At the same time, jobs that required the kinds of non-routine problem solving and complex communications skills typically seen in managerial, professional and technical occupations significantly expanded with the earnings of the college educated workers needed to fill them steadily rising.
How will the next few decades play out given the potential wider scope of AI-based automation, including the jobs of high-skilled, highly educated professionals. Will continuing advances in AI end up eliminating more jobs than they create?
In the spring of 2018, then MIT president Rafael Reif commissioned a major MIT-wide task force to address the impact of AI on jobs, economies, and society in general. After working for two years, the task force released its final report, “The Work of the Future: Building Better Jobs in an Age of Intelligent Machines,” in November of 2020.
“Technological change is simultaneously replacing existing work and creating new work,” was one of its major findings. “No compelling historical or contemporary evidence suggests that technological advances are driving us toward a jobless future. On the contrary, we anticipate that in the next two decades, industrialized countries will have more job openings than workers to fill them, and that robotics and automation will play an increasingly crucial role in closing these gaps. Nevertheless, the impact of robotics and automation on workers will not be benign. These technologies, in concert with economic incentives, policy choices, and institutional forces, will alter the set of jobs available and the skills they demand.”
A number of recent studies on the future of work have arrived at similar conclusions. For example, a December, 2017 report by McKinsey examined which jobs will likely be displaced by automation through 2030, as well as which jobs are likely to be created over the same period, based on data from 46 countries. McKinsey’s overall conclusion was that a growing technology-based economy will create a significant number of new occupations which will more than offset declines in occupations displaced by automation. However, it added that “while there may be enough work to maintain full employment to 2030 under most scenarios, the transitions will be very challenging - matching or even exceeding the scale of shifts out of agriculture and manufacturing we have seen in the past.”
But, are the rapidly improving LLMs, chatbots and related AI technologies likely to supersede these recent job projections?
The NYT article mentioned a survey by ZipRecruiter conducted in 1Q 23 that found that 62% of job seekers are concerned that AI technologies like ChatGPT could replace their jobs. The concern is highest with younger job seekers, — rising from 41% among boomers to 76% among Gen Zers, and with the less educated, —rising from 52% among those with graduate or professional degrees to 72% of those without high school diplomas.
Several research studies are now underway to start identifying the industries and occupations likely to be most exposed to these new, powerful AI technologies. Let me briefly discuss three such studies whose findings were published in March of 2023.
“How will Language Modelers like ChatGPT Affect Occupations and Industries?” aimed to assess the extent to which occupations and industries are exposed to advances in LLM capabilities. The study found that “the top occupations exposed to language modeling include telemarketers and a variety of post-secondary teachers such as English language and literature, foreign language and literature, and history teachers”; and “the top industries exposed to advances in language modeling are legal services and securities, commodities, and investments.” In addition, “occupations with higher wages are more likely to be exposed to rapid advances in language modeling from products such as ChatGPT or others.”
“Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence” is based on a randomized, controlled trial of 444 college-educated mid-level professionals to assess the productivity effects of generative AI systems on writing tasks in fields like human relations and marketing. Its results show that ChatGPT substantially raises the average productivity of those who used the technology by a substantial 37%. In addition, “Inequality between workers decreases, as ChatGPT compresses the productivity distribution by benefiting low-ability workers more. ChatGPT mostly substitutes for worker effort rather than complementing worker skills, and restructures tasks towards idea-generation and editing and away from rough-drafting.” And, ChatGPT increases job satisfaction by 20%.
“GPTs Are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models” looked at the implications of LLM-powered software and applications on the US labor market, based on the alignments of occupations with both GPT-4 capabilities and human expertise. Its findings, revealed that “around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while approximately 19% of workers may see at least 50% of their tasks impacted. … The projected effects span all wage levels, with higher-income jobs potentially facing greater exposure to LLM capabilities and LLM-powered software.”
“Our analysis suggests that, with access to an LLM, about 15% of all worker tasks in the US could be completed significantly faster at the same level of quality. When incorporating software and tooling built on top of LLMs, this share increases to between 47 and 56% of all tasks. This finding implies that LLM-powered software will have a substantial effect on scaling the economic impacts of the underlying models.”
Overall, “the impacts of LLMs like GPT-4, are likely to be pervasive,” adds the paper. “We also find that the potential impact of LLMs expands significantly when we take into account the development of complementary technologies. Collectively, these characteristics imply that Generative Pre-trained Transformers (GPTs) are general-purpose technologies (GPTs).”
Let me conclude by discussing the historical relevance of the finding that GPTs are GPTs. Over the past two centuries, general-purpose technologies have been the defining technologies of their time, given their potential to radically transform industries and economies. But, realizing their potential takes large tangible and intangible investments and a fundamental rethinking of firms and industries, including new processes, management structures, business models, and worker training. As a result, realizing the potential of a GPTs takes considerable time, often decades. The steam engine, electricity, the internal combustion engine, transistors, computers, and the internet are all examples of GPTs.
For example, after the introduction of electric power in the early1880s, it took companies 40 years to figure out how to restructure their factories to harness electric power with manufacturing innovations like the assembly line, and even longer to develop new electric household products like refrigerators, dishwashers, and washing machines. Similarly, while transistors started to replace vacuum tubes in radios, TVs, and computers in the 1950s, it took another several decades for the semiconductor industry to truly take off with the development of large varieties of consumer electronic products, as well as personal computers, smartphones, and powerful supercomputers. And, while the Internet started out as ARPANET in the late 1960s, and has since become the most significant platform for innovation the world has ever since, its further development continues in major areas like cybersecurity and 3-D interfaces.
AI technologies are clearly one of the most important GPTs of the 21st century, but we’re still in the early stages of their development and deployment. It’s only been a decade since major innovations like machine learning have finally taken AI from the lab to early marketplace adopters. And the LLMs and chatbots we’re all so excited about are barely a few years old. It promises to be a very exciting ride, but there’s much, much work ahead.
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