From the University of Chicago, Booth School of Business, Chicago Booth Review, August 19:
John H. Cochrane is a senior fellow of the Hoover Institution at Stanford University and was previously a professor of finance at Chicago Booth. This essay is adapted from a post on his blog, The Grumpy Economist.
How AI can shift supply and demand—perhaps with benefits for everyone.
As I have been reading about and discussing large language models, I find I’ve learned as much about us humans as I have about the artificial intelligence that replicates some of what we do. Introspecting, am I really that much more than an LLM?
I recognize that I have about a thousand stories. Most of my conversations and writing, especially for my blog posts, op-eds, interviews, and discussions, are built on prompts that lead to those prepackaged stories. A given prompt could easily lead to a dozen different stories, so for a while I give the illusion of freshness to someone (not my wife and kids!) who hasn’t been around me that long. House prices are high in Palo Alto, should the government subsidize people to live here? Let me tell you about the vertical supply curve.
Almost all of my stories are not original. I do a lot of reading and talking about public policy and economics, so I pick up more stories about those things than most people who have real jobs and pick up stories about something else. Learning and education are largely formal training for the acquisition of more stories to produce in response to prompts. That process is a lot like training a large language model.
This has got me thinking about programming a Grumpy Economist bot. Training an AI on the corpus of my blog, op-eds, teaching, and academic writing would probably give a darn good approximation to how I answer questions, because it’s a darn good approximation to how I work.
I wouldn’t be the first economist to be automated. George Mason University’s David Beckworth, who hosts the Macro Musings podcast, has trained a Macro Musebot on more than 400 episodes of his show. Even Milton Friedman has been conjured algorithmically, courtesy of the Friedman chatbot at the University of Texas’s Salem Center for Policy.
Now, not everything I do is complete recycling, predictable from my large body of ramblings or from what I’ve been “trained on.” Every now and then, someone asks me a question I don’t have a canned answer to. I have to think. I create a new story.
A great economist asked me for my intuition about how interest rates could raise inflation. It took a week to mull it over. I now have a good story, which helped me in writing a recent paper. Walking back with me to my office at the Hoover Institution after a seminar, Stanford’s Robert Hall asked me how government bonds could have such low returns if they are a claim to surpluses, since surpluses, like dividends, are procyclical. The notion of an “s-shaped surplus process” and a whole chapter of my recent book, The Fiscal Theory of the Price Level, emerged after a few weeks of rumination. It’s now a new story that I tell often. Perhaps too often for some of my colleagues.
This creativity seems like the human ability that AI will have a hard time replicating, though perhaps I’m deluding myself on just how original my new stories are. When I get that AI programmed up, I’ll ask it the next puzzle that comes along.
AI and the commentariat
This line of thinking leads me to recognize a part of my work that will certainly be greatly influenced by LLMs: the writing of blog posts and op-eds, the giving of interviews, and so forth. If 90 percent of what I do in that respect can be replicated, what does that mean for people in the commentary business?....
....MUCH MORE
Same.
Related:
"BloombergGPT is going to replace the analyst"
Analysts are fundamentally chat-based interfaces that senior finance professionals use to gather, organize, and output data
