Introduction
As I was in 2018, I am excited to speak to you at the National Bureau of Economic Research (NBER) artificial intelligence (AI) conference, in a city that is a world-class center of AI research and business start-ups, to discuss AI and its prospective effects on productivity and the labor market. Outside of those of us who have spent many years researching the economics of innovation, it seems that AI is having a moment. The surge in excitement and trepidation about AI is palpable. Google searches for "AI" have tripled worldwide since 2022, fueled by the buzz about ChatGPT. Of course, this group saw it coming as early as 2017, when the first NBER AI conference was held here in Toronto, and many of you saw it coming much earlier than that.
I will focus my remarks on generative AI, which creates new content largely in response to natural language prompts. As this audience knows, image and text classification-discriminative AI-has been in use for many years and is remarkably effective. I have used it to identify demographic characteristics of entrepreneurs in my own research. In contrast, effective generative AI is a very recent development and seems to be a leap forward into something new. Applications of generative AI range from the prosaic, like reducing the monotony of writing routine memos, to the wonderous, like protein structure prediction and drug discovery.
Of course, experts emphasize that at their core, all forms of AI are an exercise in prediction, and technically that is true. To the layperson, though, a chatbot that is nearly good enough to pass the Turing test is substantially different from the U.S. Postal Service using AI to read your handwriting. Some of the uses of generative AI may be unsettling. For example, concerns about the ability of generative AI to impersonate individuals to harm their reputation or violate their privacy exist and are growing. Moreover, observers have noted that AI models sometimes harbor, if not amplify, the biases found in their training data, leading to malign effects on decisions about mortgage approvals, insurance rates, medical diagnoses, and even pretrial detention. And discrimination is not just an equity issue-it also holds down economic growth, as I show in my own work.