Given the strong counter narratives regarding the impact of artificial intelligence on the economy - from bright optimism that AI will significantly lift productivity growth and wealth to dark pessimism that it will lead to a dystopic increase in unemployment and cybercrime - we asked Martin Fleming to sort it out. And the former chief economist of IBM and current author and Chief Revenue Scientist at Varicent does just that. And Mark does a bit of advertising along the way.
Given the strong counter narratives regarding the impact of artificial intelligence on the economy – from bright optimism that AI will significantly lift productivity growth and wealth to dark pessimism that it will lead to a dystopic increase in unemployment and cybercrime – we asked Martin Fleming to sort it out. And the former chief economist of IBM and current author and Chief Revenue Scientist at Varicent does just that. And Mark does a bit of advertising along the way.
For more about Martin Fleming, click here
For more on Martin Fleming's book, Breakthrough: A Growth Revolution, click here
To participate in the weekly Survey of Business Confidence, click here
Follow Mark Zandi @MarkZandi, Cris deRitis @MiddleWayEcon, and Marisa DiNatale on LinkedIn for additional insight.
Mark Zandi: Welcome to Inside Economics. I'm Mark Zandi, the chief economist of Moody's Analytics, and I'm joined by my two trusty co-hosts, Cris deRitis and Marisa DiNatale.
Hi, guys.
Cris deRitis: Hey, Mark.
Marisa DiNatale: Hi, Mark. Long time no see.
Mark Zandi: Do you notice that I've got this down now, this introduction, like Walter Cronkite? What did he say when he signed off every night? Oh, come on, you guys don't remember?
Marisa DiNatale: "Good night and good luck," or something like that?
Mark Zandi: No, shoot, I don't remember now either.
Cris deRitis: "And that's the way it is."
Marisa DiNatale: That's somebody else.
Mark Zandi: That's the way it is. Yeah, that's the way it is. I think I've got to gotten this down, this introduction.
Cris deRitis: Are you sort of comparing yourself to Walter Cronkite? Did I just hear that it?
Mark Zandi: Yeah, it does sound like a humility, doesn't it?
Cris deRitis: No. That's good. That's good. Aim high. Aim high.
Mark Zandi: Yeah. Actually, my wife has this saying, and I've been trying to figure out a way to introduce it into the podcast. I just haven't been able to figure it out, so I'm just going to do it.
Cris deRitis: Just say it. Yeah, just say it.
Mark Zandi: I'm going to give her credit for it. It's a Yogi Berra kind of comment. It's like, "I don't have any idea what I'm talking about, but I could be right."
Marisa DiNatale: That's great.
Mark Zandi: I could be right. That is the greatest line of all time.
Marisa DiNatale: She made that up?
Mark Zandi: She made that up. Well, it's funny because I don't really read the popular press. I just don't have time to do that. I read articles around the economy and policy and politics, but I don't read broadly, and so her job every night is to be like Twitter for me, X, you know what I'm saying, to kind of summarize all the news.
Marisa DiNatale: She's in charge of pop culture?
Mark Zandi: The first thing I ask when she says something to me is, "Okay, where did you read that because that's key to me. What's the source?" and then I start peppering her with questions. It's really unfair because she's doing me a service and I'm berating her, and then that's where that line came from. I have no idea what I'm talking about, but I could be right. I could be right. I thought that was very good.
Hey, we've got a great podcast. We've got a great guest, Martin Fleming. We're going to go to him to talk about artificial intelligence, AI, and that was a pretty long conversation, so I don't want to take too much time here, but I do want to play the statistics game, and I do want to make a couple of advertisements. Advertisement number one is... What is advertisement number one? Oh, we have a conference. We have two conferences, one in Chicago and one in Dallas. You guys, I think the Chicago conference is the 12th?
Marisa DiNatale: It's Tuesday.
Mark Zandi: Tuesday the 12th, and the conference in Dallas is...
Marisa DiNatale: The following week.
Mark Zandi: ... the 27th, I believe, something like that. You guys correct me if I'm wrong.
Marisa DiNatale: Oh, no. Yeah. 26th? 27th?
Mark Zandi: No, I think it's the 27th. There you go. I have no idea what I'm talking about, but I could be right.
Marisa DiNatale: You could be right.
Mark Zandi: I could be right. 26th is a day. It's September. Yes, it is.
Marisa DiNatale: It starts with a two.
Mark Zandi: Yeah, I think it is the 27th.
Cris deRitis: 27. I have 27th.
Mark Zandi: It is the 27th. Okay. Please join us. Will you be at both conferences, Marisa, or just the one in Chicago?
Marisa DiNatale: Just Chicago next week. Yep.
Mark Zandi: Adam is going to be taking place in Dallas.
Marisa DiNatale: That's right.
Mark Zandi: Okay, but, Cris and I, we're at both conferences, right?
Cris deRitis: Yes.
Mark Zandi: Yeah. Okay, so that's advertisement number one. Advertisement number two, we have this survey of business confidence. We've been conducting this weekly for 20 years, back to 2003. I can remember when I put the questions together. There's a bunch of questions around hiring, investment, broad questions about how business is performing today, how's it going to perform in the future, financial conditions, so forth and so on. It's a very valuable survey, and we need participants.
Cris, how can people participate if they want to? What's the best way for them to do that if they wanted to participate?
Cris deRitis: Yeah, they can go to economy.com. They should see a link there. I believe it's economy.com/sbc. It will take you there directly.
Mark Zandi: Survey business confidence, SBC, yeah, economy.com.
Cris deRitis: Economy.com, I think it's there.
Mark Zandi: Yeah. Okay, so please, we'd really love for you to become a participant in that survey. Very valuable, timely information, we do the survey. The survey we're doing this week will be published next Monday, so you get the results. I find them very valuable because it's so timely. I track that and write that release every week. I've been doing that for 20-plus years, so that's very important.
Also, we had a webinar. Now, we're outside the advertisements, but we had this webinar yesterday, the three of us, on the US macro outlook. Here's my question to each of you, and I'll begin with you, Cris.
Cris deRitis: Cool.
Mark Zandi: Yeah. Of all the things that we discussed in that webinar, was there anything that surprised you, that you learned, that you didn't know before or you go, "Oh, that's interesting. That was insightful," anything particularly what I said? Did I say anything that was insightful to you that you learned from the conversation?
Cris deRitis: Mark, you're always insightful, but Marisa wowed me with this, oh, very interesting set of charts on China, the trade linkages, decoupling that was going on, so I'd highly recommend it just for the chart itself. Mind blowing.
Mark Zandi: You're talking about the starburst chart that shows linkages across industries and countries over time. That one.
Cris deRitis: Yes. 9 million products, was that it, Marisa?
Marisa DiNatale: Yeah, and I did not make that chart to be clear. That chart was made by two of our colleagues, Harry Murphy Cruise and Steve Cochrane, the team in Asia-Pac, and it's from a great paper that they wrote about decoupling between the US and China. Yeah. I think my section also wowed me. That's what I learned the most about, too. Yeah, I think the China discussion and the history of decoupling, I had to dig into it in order to present that part, and it was very interesting.
Mark Zandi: Yeah. Actually, I was going to say the same thing. I thought that was a very interesting part of the conversation and, that webinar, we taped that I believe so folks can go take a gander. I think you'll find that of some interest and some value.
Okay. Let's play the statistics game before we move on to our conversation with Martin. The game is we each put forward a statistic. The rest of the group tries to figure that out through questions, deductive reasoning and clues, and the best statistic is one that's not so easy. We get it immediately, not so hard that we never get it and is apropos to the topic at hand or related to recent data releases.
This was a tough week because it was short. We had Labor Day and, also, this week is dry on a lot of statistics. I guess I'm apologizing for my statistic already, but anyway, Marisa, tradition has it. We go with you first. What's your statistic?
Marisa DiNatale: Three and a half percent.
Mark Zandi: Oh, my gosh.
Marisa DiNatale: I know.
Mark Zandi: No way. No way. No, it can't possibly be my statistic.
Marisa DiNatale: It's apropos.
Mark Zandi: Oh, I can't believe it. Okay.
Cris deRitis: Oh, gosh. Well, come on.
Mark Zandi: This is going to kill me if this is the case. Okay, there's a lot of three and a half percent though. Okay, three and half percent.
Cris deRitis: This one goes right to the top.
Marisa DiNatale: Right there. That came out this week.
Mark Zandi: I don't know. It's the year-over-year growth. Correct?
Marisa DiNatale: No.
Cris deRitis: No.
Mark Zandi: Okay. Okay. Okay, then my statistic is also three and a half percent. No lying.
Marisa DiNatale: Interesting. Okay.
Mark Zandi: No lying. Okay, so three and a half percent is-
Marisa DiNatale: Cris knows what it is.
Mark Zandi: Oh, he does?
Cris deRitis: Yeah, but go ahead, Mark. You want to announce it? It's the poorly annualized growth through output per hour, right?
Marisa DiNatale: That's right.
Mark Zandi: Okay. No. Go ahead, Cris. Go ahead. I bow to you. Go ahead.
Cris deRitis: I just gave it output per hour productivity growth.
Marisa DiNatale: Yeah, it's annualized productivity growth in the second quarter.
Mark Zandi: Oh, that's so boring.
Marisa DiNatale: I know, but it went with the topic at hand perfectly.
Mark Zandi: That's true. That's true. That's a good point.
Marisa DiNatale: That's why I picked it.
Mark Zandi: So what is the number, Marisa?
Marisa DiNatale: There wasn't much else interesting to pick. It's productivity growth in the second quarter. This is a revised figure. This had already been released, so this is the revision to the productivity number, three and a half percent. It's the fastest annualized productivity growth that we've seen since coming right out of the pandemic in 2020, and it's well higher than what exists kind of prevailing productivity growth prior to the pandemic. Now, this can be volatile from quarter to quarter, so I don't want to make too much of it. Productivity growth on a year-over-year basis is up 1.3%, which is pretty weak, but pretty in line with what we've been seeing the last several years.
One of the interesting aspects of this is that part of the reason productivity growth is so high is because hours worked fell quite a bit, because this is output per worker hour. We've seen from the BLS average weekly hour numbers that the average work week has been trending lower now for quite some time. It's pretty low, so this partially is reflecting the numerator, which is hours, and that has been contracting.
Mark Zandi: That's a good one. Yeah. Now that I looked at that carefully, that just reestablishes what we consider to be the underlying trend rate of growth of productivity, about one point a half percent per annum, and that's what it has been since the pandemic hit. If you go back to the Q4 2019 or Q1 2020. It had been slumping a little bit, I think, and now it's come right back up to about one point a half percent. That's a good statistic particularly, you're right, in the context of the conversation we're going to have about AI because that is going to be key to future productivity growth.
Cris, what's your statistic?
Cris deRitis: All right, I went back to the vault for this one. Okay, $3 and 69 cents.
Mark Zandi: Is that the price of copper?
Cris deRitis: You got it.
Mark Zandi: Oh, wow.
Cris deRitis: Yeah, you redeemed yourself.
Mark Zandi: Yeah, I think the vault helped me out there when you said the vault.
Cris deRitis: Yeah. Yeah.
Mark Zandi: So this is your statistic? This is my statistic. We go all the way back-
Cris deRitis: Two years ago.
Mark Zandi: ... yeah, to the start of the podcast.
Cris deRitis: Right. Watch copper.
Mark Zandi: Why'd you pick that?
Cris deRitis: 360, it's actually down from the peak. A year or so ago, it was closer to four and a half bucks, so it does show moderating, but it's still above the level that we had in 2019, which was just under $3, and so I wanted to know from you/ I picked it because I want to ask you a question. What do you think is the breakeven or what's the threshold for recession issues?
Mark Zandi: Yeah, I think we were at $2 for recession. Three was the equilibrium price pre-pandemic. Anything over four was considered to be boom, and maybe you said this and I missed it, but the context is Dr. Copper. Copper is a very sensitive price to global demand and supply. When the economy is strong, there's a lot of demand for copper, price goes up, and so if you're over four bucks, you're boom, and three bucks was typical. Two bucks was recession. I think I'd shift that up by a dollar. I think recession would be below three, typical around four. Above five is boom-like. It feels like we're on the soft side of typical.
The reason why it's up about a buck, I think it's supply and demand. I think demand has been juiced a little bit by green-
Cris deRitis: Yeah, electrification.
Mark Zandi: ... technology investment. There's a lot of demand for it. I think the supply has been a little bit more limited. A lot of it comes from Chile and there are some, I think, production issues and things like that. Correct me or tell me if you have a different view, but my sense is that what you used to call the breakeven price consistent with a typical economy would be around four bucks for copper, and we're just south of that right now.
Does that sound about right? What do you think?
Cris deRitis: Yeah, I think that's reasonable. Yeah, the goalpost certainly has been raised though. It has been raised. That's the main point.
Mark Zandi: I think commodities, in general, although oil has picked up most recently, but commodity prices in general are a little depressed, somewhat depressed, and I think that goes back to China, our conversation around China. China's economy has been weak, and they're obviously sucking down a lot of commodities. If they're not buying a lot of commodities, that means prices are going to be soft as well. I think the copper price reflects that.
Okay, back to my statistic. It's so uncanny, 3.5% year-over-year.
Cris deRitis: It came out this week.
Mark Zandi: It did, indeed.
Cris deRitis: Government statistic?
Mark Zandi: Nope.
Cris deRitis: Oh, okay. That's the big hint?
Mark Zandi: I'll give you another hint if you need it.
Marisa DiNatale: Housing market related?
Mark Zandi: Nope.
Cris deRitis: Auto market related?
Mark Zandi: I'm going to say no because, at first blush, it's not, although there is information in this data that goes to the housing market and the audit market. That's a big hint, by the way.
Marisa DiNatale: Oh, it's not an interest rate. You said it's a growth rate.
Mark Zandi: It's a year-over-year growth rate through, here's another hint, August. It just came out. It's not a government statistic. It is a Moody's statistic.
Cris deRitis: Oh, come on.
Mark Zandi: Cris, I'm playing the statistic at your feet.
Cris deRitis: Moody's, it's not survey business confidence related, is it?
Mark Zandi: No. No. No.
Cris deRitis: Well, that would've been clever to work it out.
Mark Zandi: It would've been clever actually. It would've been clever. I'm not that clever.
Cris deRitis: A Moody's statistic.
Marisa DiNatale: Moody's, meaning, our group, us, economics or something outside of us?
Mark Zandi: Yeah. Yeah. It's outside of us. Yeah. All right, you guys give? No, you don't give. Okay. Let me give you one more.
Cris deRitis: Yeah, I'm trying to think what it is.
Mark Zandi: One other hint that won't give it completely away, we also do this in conjunction with another firm.
Marisa DiNatale: Oh, is it Equifax?
Mark Zandi: It's Equifax.
Marisa DiNatale: So it's credit?
Mark Zandi: Yeah, it's year-over-year growth.
Marisa DiNatale: Growth in credit outstanding?
Mark Zandi: Total household debt outstanding.
Marisa DiNatale: Debt outstanding. Okay.
Mark Zandi: The whole shooting match, everything.
Marisa DiNatale: Got it.
Mark Zandi: Car auto, that's why I said auto, residential mortgage, student loans, bank cards-
Cris deRitis: Personal loan, yeah.
Mark Zandi: ... personal finance, the whole shooting match, I'm making this up, roughly speaking, $16.4 trillion in household debt outstanding, up 3.5% year-over-year. That's news because that's actually a pretty slow growth. We're seeing a very significant slowing in the growth in household debt, in liabilities, particularly since-
Cris deRitis: That's including mortgage though, right?
Mark Zandi: That is include mortgage, but every product line, the growth rates are down. They're down for consumer finance. They're down for bank card. They're down for auto, they're down for first mortgage. They're down for home equity.
Marisa DiNatale: They're slowing.
Cris deRitis: Slowing, yeah.
Mark Zandi: The growth rates are down. Yeah. Student loans, maybe you know more than I, it's actually declining. I don't know if that's related to debt forgiveness. We're starting to see some impact from some of the debt forgiveness that's showing up in the data or maybe some reporting issues, but 3.5%, that's income growth. That means the household debt is not adding to the debt load at this point. It had been 2021, 2022, coming into this year, consumers households were borrowing aggressively, saw big increases in debt outstanding, but now it's pretty much back to what you consider to be typical, which I view is another positive sign, I think. Maybe it's somewhat demand and supply, demand in the sense that you don't need the credit because wage growth is now stronger than inflation. Therefore, people's real wages, purchasing power are rising, so they don't need to borrow. That would be a good interpretation.
A more vexed interpretation is, well, supply, particularly since the March banking crisis when lenders have tightened up standards, and so therefore they're just not extending out as much credit. Either way, household debt loads are no longer getting heavier. I do expect that, if this continues, and I would expect it to continue, that credit quality, meaning, delinquencies, should start to moderate here in the next six, 12 months, something like that.
What do you think, Cris? You follow that data very carefully. What do you think of my interpretation of the data?
Cris deRitis: Yeah, I'm a little bit more-
Mark Zandi: I knew you would be. Yeah.
Cris deRitis: ... concerned. Yeah. The income earners at 3.5% might be a very different group than the borrowers at three point half percent.
Mark Zandi: True. True.
Cris deRitis: I think you need to be a little careful in terms of looking at the aggregates.
Mark Zandi: Although I will say I'm going to throw one of your charts back at you. You've got this great chart showing wage growth by income group relative to inflation, and wage growth is up across all income groups, particularly for low income groups, low wage groups. Right?
Cris deRitis: Yep. That's true. Yep.
Mark Zandi: Well, very good. That was a great discussion. I think, at this point, you guys have got anything else you want to say before I bring in Martin Fleming to talk about AI? You guys good?
Cris deRitis: No. I want to hear about AI.
Mark Zandi: Let's talk about AI, so let's bring in Martin Fleming. Let me bring Martin Fleming into the conversation.
Hey, Martin, how are you?
Martin Fleming: I'm fine, Mark. How are you?
Mark Zandi: Good. I'm very happy to have you on Inside Economics. I've been trying to get you on for a while. You've been very elusive. I don't know. What's that all about?
Martin Fleming: Well, I'm pleased to be with you nonetheless despite the elusiveness.
Mark Zandi: It's very good. We've known each other, I don't know, for how many years we were on the council of... I think it's called the Council of Business Conference.
Martin Fleming: Conference of Business Economists, doesn't have much of a public presence. My wife calls it the mystery organization, but it's really 50 chief economists who meet three times a year. It's quite useful to engage with our colleagues, I find, and I'm sure you do as well.
Mark Zandi: Absolutely. I think you joined when you were chief economist of IBM, I believe.
Martin Fleming: I was, yes. I spent 10 years as chief economist and chief analytics officer at IBM and left in the middle of the pandemic to pursue a career in software and research.
Mark Zandi: Right, and now you've got a full plate. You're at the Productivity Institute. You're a fellow there. We've got to explore this a little bit. You're the chief revenue scientist for Varicent, which is I think a Canadian software company. Right?
Martin Fleming: Yes. Correct. Yeah.
Mark Zandi: Yeah. Do you want to just describe a little bit about your position at the Productivity Institute and also what you're doing with Varicent?
Martin Fleming: Sure. Productivity Institute first, as the name suggests, the UK government has funded a research institute to focus on the issue of productivity which, as I'm sure all of the listeners will know, is suffering in the UK, so there's been a very robust research effort underway. For me, like many researchers, I have a portfolio of projects that I'm engaged in, some of which through the Productivity Institute.
I also spend time with the Bureau of Economic Analysis, BEA, which is part of the commerce department, that estimates the GDP and the national income and product accounts focused on software prices and data center investment, and at the MIT-IBM lab, which is the focus on artificial intelligence. That bleeds over into my role at Varicent, which is the more practical application.
Varicent is a sales performance management firm. It turns out that sellers generate a great deal of data which is, of course, the scarce resource in all of this and the need to be able to, number one, pay sellers through relatively complex sales plans in a timely and accurate way turns out to be critically important if you want to keep sellers happy and, number two, to be able to structure sales territories and quotas in a way that allows for us to be able to use machine learning and artificial intelligence to predict and optimize both the territories, their quotas and their account potential. It's one business process where the new technology is being rapidly and quickly deployed to help sales leaders and senior business leaders be able to generate greater return from their sales investment.
Mark Zandi: You said that Moody's, I didn't know this, Moody's is a client of Varicent.
Martin Fleming: Sure. Yeah, there are a large number of salespeople who have relationships in turn with your clients who we provide the support both for their sales planning and territory and quota management, as well as for their incentive compensation.
Mark Zandi: Well, I'm glad to be your client.
Martin Fleming: ... and we are pleased to have you.
Mark Zandi: Yeah, Moody's is a good client. Hey, and you've written a book, too.
Martin Fleming: I have.
Mark Zandi: You want to give us a sense of the book?
Martin Fleming: Sure. The book is called Breakthrough, A Growth Revolution, and it focuses on industrial revolution. I go through and define quite carefully and tell the history and the story around three industrial revolutions that have now been completed and the fourth industrial revolution, where we are currently working our way through, and develop the economics of industrial revolution.
As economists, we oftentimes tend to think of growth as an unending long-term trend upward, but in fact, when we look at the history, we find out that there are quite significant variations in long-term growth responding to a variety of economic trends around technology, around the adoption and diffusion of the technology, the transformation and migration of business models, the transformation of the labor market, and worker skills and workers' interest and ability to adjust, and the role that government policy plays across each of the industrial revolutions. It's I believe a helpful guide for understanding where we are at a point in time and how both business and public policy is responding to existing conditions.
Mark Zandi: Well, this brings us to the topic at hand, AI, artificial intelligence, and you are uniquely positioned to help us try to understand this new technology and what it means for the economy more broadly. There's a lot of angst around this, and it feels like a lot of hope, a lot of optimism, but also a lot of hand wringing and a lot of angst. I'm hoping we can dig through that. I thought we could start though with the basics and just defining AI.
Let me just preface this by saying, and I'm curious what you think of this, I think I've been doing AI for a long time. My first project when I first started with my brother and a good friend a company that ultimately we sold to Moody's, the first project was with this bank, the Shamil Bank in New England.
Cris, Marisa, have I told this story before?
Martin Fleming: I don't-
Mark Zandi: You don't recall it?
Martin Fleming: ... think I've heard of it. No.
Mark Zandi: Okay, let me just tell the story real quick and, Martin, you tell me if I've been doing AI or not. Back then, this was in the early '90s, the interstate banking was just starting and banks were quickly moving outside of their footprint and acquiring other banks. To acquire though, you needed to get permission from the Federal Reserve, and one of the criteria the Fed used was around discrimination. Were you discriminating in your lending, particularly mortgage lending? They had this model that they had developed to identify if a bank was discriminating or not. I won't go into details there, but anyway, the bank asked, "Could I take a look at what the Fed had done?" and I used a neural net at the time to help with that modeling because the modeling was very complex. I wanted to see if there was different interaction terms and non-linearities, things that could not be picked up. Well, if you didn't have that kind of capability, it would be very difficult to pick that up.
In fact, I did, and I found that after you controlled for some of these other relationships that the neural net discovered that hard to conclude one way or the other where the bank was discriminating or not. At the end of the day, the Fed said they were. They couldn't acquire and they were acquired, so they lost that battle, but I learned about neural nets at that point. Was that AI?
Martin Fleming: Yeah. Absolutely. Neural nets are at the heart of deep learning and artificial intelligence. What you were doing was predicting whether or not the bank was discriminating or not discriminating. It's the prediction that's at the heart of artificial intelligence. The simplest example that we all encounter I'm sure every day is when you're trying to type a text on your phone and your phone suggests the word that you're trying to type. That's a small machine learning algorithm in your phone that's predicting the word you're trying to type. It's just like when you get on Netflix and Netflix makes a recommendation and predicts what video you might like to see. That's artificial intelligence. In fact, Netflix has a substantial team focused on artificial intelligence, and they obviously at this point have a huge amount of data. That simple idea of prediction is really at the heart of artificial intelligence.
Now, recently, over the past eight, nine, 10 months, large language models have gotten a lot of attention, and that's really what has spurred all of the activity. We think of those large language models as part of generative AI. I'm going to distinguish between generative AI and classical AI as you were applying it in the financial services context. Generative AI, number one, has a large set of applications. Think of question and answers, customer operations. We all dial into call centers with questions and we ask the same question in different ways. It's important for the call center to be able to give an accurate and precise answer, in some cases for legal and medical reasons, but for customer relations as well.
The ability of natural language processing and large language models to take all variety of the same question and give exactly the precise answer is very important, and it's highly productive because it helps those who are working in the call center to be able to provide better service to the client who has the question. We're finding, of course, large language models are very important in software engineering, be able to write code more efficiently. The productivity of software developers is extremely important particularly as we continue to expand these capabilities. We've seen a lot of early applications in marketing and sales being able to write pieces both for marketing purposes, but also just for emails to be able to answer questions and be more efficient in responding to emails. The more-
Mark Zandi: So... Oh, sorry, go ahead. Go ahead.
Martin Fleming: Well, I was going to say the more classical AI, and we spoke earlier about Varicent, it would be a great example of more classical AI where we have a large body of data, and we want to help our clients be able to make decisions quicker and more effectively to be able to manage their sales, their sales forces so that the sellers can earn more income, be more productive, sales leaders can be more effective and, overall, a more profitable revenue can be generated by the organization as a whole.
A really fascinating illustration is a company called FreeWire. FreeWire is based in Northern California, Silicon Valley. It's an electronic vehicle charging station company. It turns out, for charging stations for EVs, finding the right locations is a big problem. They've developed a set of algorithms, artificial intelligence algorithms, to identify the locations which optimize the ability of drivers, whether it's cars or trucks or SUVs, to be able to recharge in a timely way.
The interesting aspect is many of these locations are gas stations. They're folks who maybe own 15 gas stations, the owners of which have no idea about artificial intelligence, have no idea of the science behind it, but what they've come to realize is that, if they follow the recommendation of FreeWire, they generate an enormous amount of cash flow and they improve the profitability of their business by using the application. It's a great illustration of the science being in the background by delivering the business benefits to folks who have no familiarity, no training in mathematics or data science.
Mark Zandi: In some sense, AI has been with us for quite some time. I was doing some variation on the theme in a very crude way because the computing power and the data availability was not nearly what we have today back in the early '90s. It's still kind of, sort of the same species as what the AI exist today, but it seems like it's come out of nowhere. I mean, it feels like, obviously, everyone's talking about it in the context of, well, our everyday lives, the job market, what it means for cybersecurity and also, just looking at what's happening to stock prices. Is that because of ChatGPT, the fact that it just all of a sudden became a tool that everyone could use? What's behind this? All of a sudden, at least for me, seemingly out of nowhere it has come to the fore.
Martin Fleming: Sure. No. That was certainly a significant event gaining public attention, but the reason why ChatGPT could do what they have done were the constraints that were previously faced by these applications around the compute capability and compute power and the availability of data all of a sudden became much less constraining. One of the early applications, in fact, at the MIT-IBM lab, we've written a case study around the use of AI with a European grocer. Grocers, of course, have significant supply chain issues. As consumers, we don't want to go into a grocery store and find out that the product we're looking for is not available. The grocer doesn't want to have the product not available because of lost sales, but it turns out that, with a tremendous number of SKUs in a grocery store and a large number of grocery stores and sewer locations for most chains, it requires a tremendous amount of computing to be able to solve that problem.
That computing constraint still exists to a great extent, but it's much more readily available, number one, with the advent of Amazon Web Services, the Amazon Cloud, the Microsoft Cloud, the Google Cloud. These public clouds have allowed for these applications to be able to be deployed at quite significant scale, and then as part of that, Nvidia, of course, is getting a great deal of attention these days because of the availability of graphical processing units which started off in gaming, but are very important for solving the kind of problem that you were originally outlining because it's all about linear algebra. You were doing linear algebra to solve your neural network model.
It turns out that the more common CPUs, the Intel type product, is not very good at doing that at speed, and the graphical processing units that Nvidia has gotten so much attention around and the other semiconductor companies are now trying to catch up to has been very important. In addition to compute is the availability of data. Now, with the advent of the web, you can go out and scrape websites and amass enormous amounts of data. Now, there are problems in doing that, which we can talk about, but the data constraint is somewhat less in the generative AI space because of the availability of so much data.
Mark Zandi: So just to summarize, what you're saying is this exploded onto the scene because a bunch of stuff came together. One, we had this cloud computing that gives us enormous computing power and then we got the processors, you mentioned Nvidia as the poster child for that, that allow for the computations and then, third, data, the explosion of the data. You bring all these three ingredients together and that allows for AI to take off.
Martin Fleming: Absolutely.
Mark Zandi: Yep. Okay. I got a statistic for you. I got a question for you. How many days did it take for ChatGPT to get a hundred million users?
Martin Fleming: I'm probably not going to get the precise answer, but I seem to recall it was 30.
Mark Zandi: Okay. Cris, Marisa, you want to take a stab at that? How many days did it take for ChatGPT, and ChatGPT was introduced in November of 2022.
Cris deRitis: Let's go with the week.
Mark Zandi: Seven days. Okay. Well, you mean seven or five? Five workday or seven days?
Cris deRitis: Let's go with seven. It available on the weekend.
Mark Zandi: It's available on the weekend. Okay, seven. Okay. Marisa?
Marisa DiNatale: I'll go right down the middle, two weeks.
Mark Zandi: Two days.
Marisa DiNatale: Two weeks.
Mark Zandi: Two days.
Cris deRitis: 14 days.
Mark Zandi: Two days is the answer.
Marisa DiNatale: The correct answer is two days?
Mark Zandi: Yeah. The correct answer is two days. Just to give context, how many days did it take TikTok to get to a hundred million users in the context of two? Now you've got two days. Now, what is Tiktok?
Martin Fleming: Yeah, I would say a year.
Mark Zandi: Nine.
Martin Fleming: Nine days.
Mark Zandi: Nine days. Okay, one more, I keep going, but I'll to do one more, Instagram. How many days did it take?
Martin Fleming: Instagram? Well, both of my previous answers have been way too long. I'll have to say three months.
Mark Zandi: 30 days.
Martin Fleming: 30 days? Okay.
Marisa DiNatale: Wow.
Mark Zandi: Two days. Two days, I mean, it gives you a sense of how amazing that is. Yeah, it's just explosive. Okay. Let's now turn to what does it mean. Because this is a podcast about the economy, my mind immediately goes to, and this goes to your position at the Productivity Institute, what does it mean for productivity growth? Here, you've got countervailing narratives. One narrative is saying, okay, productivity growth, we need it. Productivity growth has been under a lot of pressure. You mentioned, in the UK. It's been basically non-existent. Here in the US, it's been better, but it's still slow relative to historical norms.
We need these kinds of new technologies like AI to come on to help support productivity growth particularly in the context of a slowing growth in the labor force, the aging of the population. If we have fewer people working, if we're going to produce the same amount, we need them to be able to produce more productively, so they say, "Bring it on. We need the AI," and then the other narrative is just the opposite. "Oh, this is going to wipe out so many jobs. It's going to be dystopic. We're going to have unemployed people all over the place."
In fact, one more story, and I'm going to brag just a little bit. Well, I guess I'm bragging. I was on a panel with Mark Cuban. Did I tell you this story guys before? You don't remember any of my stories anyway I could have told you five times.
Martin, they don't remember any of my good stories. I was on a panel with Mark Cuban. It could have been 10 years ago. He has been investing aggressively in AI companies. I don't know if he still is, but he was at the time, and he was saying how dystopic this was going to be, that basically, "Zandi, you're an idiot. The problem isn't going to be low unemployment. It's going to be mass unemployment all over the place." You've got smart people saying both these things. Martin, what do you say? What does this mean for underlying productivity growth?
Martin Fleming: Yeah. Let's talk about the productivity issue first and then the implications for the labor market because I think having a little bit of background will help the labor market discussion. This is really the topic that I address in my book, Breakthrough, A Growth Revolution. What we see in each of the industrial revolutions are really four common events or characteristics that result in success. If the fourth industrial revolution is going to yield the productivity, the economic growth and perhaps a slightly more even distribution of income and wealth, there are certain criteria that I assert have to be addressed. First is, of course, the deployment of the technology.
Today, despite all of the promise and hope of artificial intelligence, its use is largely limited to the software sector, if you will, call it the technology sector. When I say the software sector, I would include firms like Amazon, Microsoft and Google even though they're both on the data center and software side, but a large number of software firms who are building and beginning to deploy these capabilities, number one. Number two, a relatively small number of large organizations, as you know I'm sure, Mark, there are less than 300 businesses in the US that have more than 5,000 employees. It's these large organizations that have the skill and capability, but in order to have a meaningful economic impact, we need widespread deployment across all large businesses, medium businesses and small businesses, so that's why the example I shared with you earlier around FreeWire.
Deploying the benefits of artificial intelligence to franchisees who own filling stations and gas stations across the nation is a great example of the kind of diffusion that is necessary and, for the franchisees, a new business model. They're getting into a new business of recharging electric vehicles and not just pumping gas and providing convenience stores. It's a great example of the kind of diffusion that's necessary, number one, and, number two, business model transformation. The third is the transformation of how work gets done.
Now, here's another place where, like with the technology and with the early stages of business model transformation, we begin to see a little bit of optimism. In part, it has been spurred on by the pandemic. We have now, number one, greater capabilities of folks to work from home, but more importantly perhaps, number two, we have 115 million workers in the US who have quit their jobs over the past two years.
Mark Zandi: Is it that high? Is it really?
Martin Fleming: Sure. You just add it up in the JOLTS data. Now, of course, some of them left the labor force, some of them maybe quit twice, but most of them found new jobs. Whether it's 115 million or 75 million, we're talking about more than half the US labor force has turned over in two years. What does that mean? That means that workers presumably have now found new positions where they have greater satisfaction, improved compensation, perhaps other improvements to other benefits, maybe better work-life balance. You would argue that they would be now more fully engaged and, therefore, there's some possibility to be more productive because these workers have moved into positions that they have greater satisfaction around and are more open to using the new tools and capabilities that are becoming available through the deployment of digital technology, including artificial intelligence.
The fourth criteria is public policy. In the US, we've seen, of course, as is well known, and you've been deeply involved in all of this, the Inflation Reduction Act, the CHIPS Act, the Infrastructure Act, no guarantee that all of those projects will be successful in yield and economic and social rate of return necessary, but it's certainly characteristic of periods of industrial revolution to have the public sector provide renewed infrastructure to facilitate both the transformation of how work is done and how business models are deployed. As these events occur in the years ahead, we'll determine whether or not we see sustained improvements in growth and productivity.
Mark Zandi: Let me ask, Goldman Sachs has done some really good work here, and they came out with a study, I don't know, a couple, three months ago. I'm sure you're aware of it.
Martin Fleming: I'm well aware of it.
Mark Zandi: Just for the listener, and I may have characterized this wrong because it actually was written in a way that was a little odd, so I'm not sure I got it exactly right. Their headliner was that AI ultimately, and ultimately wasn't clearly defined, but ultimately would lift underlying productivity growth in the United States by 1.5% per annum. That's on top of the existing productivity growth, which just so happens to be 1.5% per annum. In my mind, that means you add the two together and you get 3% productivity growth. That's very strong. There's really only two other periods where the US has experienced consistent 3%-plus productivity growth. One was late '90s, early 2000s when the internet was being fully adopted or came onto the scene, and the other was in the '20s under electrification, and we saw a long period of strong productivity growth.
That's just the number. My question to you is is that in the ballpark? Does that sound right to you? How do you think about that in the context of those four factors and how they're playing out?
Martin Fleming: Yeah. Sure. Their work has gotten a great deal of attention, and I see their work. It seems like they send out work four or five times a day, but this one in particular-
Mark Zandi: Good work, by the way. I mean, it's actually good work.
Martin Fleming: It's excellent. It's excellent.
Mark Zandi: Yeah, it's excellent.
Martin Fleming: Yan and the team team have done great work. Just to be precise, what they said was a 1.5 percentage point improvement in 10 years after, and here's the critical nuance, after widespread deployment.
Mark Zandi: Yeah, after widespread deployment. Yeah.
Martin Fleming: Now, what does that mean? I just went through the four criteria that are necessary to achieve widespread deployment. If we can do all of the four, if we can go through the massive economic, social and business transformation I just outlined and achieve widespread deployment, then in 10 years, which I would say takes us to something like 2040, we would over the course of the 2030s see a percentage point and a half improvement in productivity.
Now, what about the percent and a half? You're right that in the 1920s, which by the way was the comparable period of the second industrial revolution as we're in for the fourth industrial revolution, there was significant improvement of productivity. The comparable period in the third industrial revolution, which was the late 1940s, 1950s, '60s through the early 1970s, we didn't quite get to 3%, but it was two point and a half percent over that time period. If I had been writing it, I probably would not have said a percent and a half. I might've said a percent, and I can see productivity growth going from what has been 1.4, 1.5% up to 2.5%. 3% seems very ambitious, but it's in the ballpark of what's possible-
Mark Zandi: Imaginable.
Martin Fleming: ... in 10 years after widespread deployment.
Mark Zandi: Okay. Yeah, that makes a lot of sense to me. I think the other thing that matters a lot, and I don't know if this fits into your four criteria, I'm sure it does, is new businesses form and they form around, they optimize around new technology. Right now, businesses like your business, like our business is aggressively trying to bring AI into our business practices, but that's a process and we have to figure that out and we got to move things around. We don't have the right skill sets and we got to get the right skill. There's a lot of things going on to make that work, but when a new business forms, they don't have all that legacy stuff. The business model is a new business model and they can optimize around AI, but that takes time. That doesn't happen next year. That happens 10, 20 years from now. That's kind of what you're saying.
Martin Fleming: Absolutely. I guess the qualification I would add, and I go through this in a lot of detail in the book. John Haltiwanger, who you know, has been focused on the new business formation issue as a source of productivity growth, which is absolutely right. You're right, many new businesses fail, but those who succeed can be highly productive and add to productivity. However, the largest number of businesses are those who continue to exist. We need both the new businesses to be new and more productive and deploy new business models, but we need the existing businesses to give up the old ways and transform to the new ways.
Now, in part, that's what the pandemic did. In part, the pandemic allowed us to take a pause, obviously, for not good reason, and nobody has reached, not nobody, but very few businesses have returned to doing things the way they did it in 2019 and, 2023, four years later, we're seeing significant transformation. It's often characteristic of these industrial revolutions. World War II is a great example where businesses really ceased to function in the US in the way they had prior to the war and moved to a wartime footing and, in the 1950s, didn't go back to the way they were doing things in the 1930s, but deployed the new manufacturing and fossil fuel technology, and that led to enormous growth, and there's a possibility of seeing a similar sequence of events in the current decades.
Mark Zandi: One thing that might, I'm just going to try out a theory on you, that might accelerate the adoption of AI and have more significant productivity benefits more quickly is the surge in stock prices for companies that either are involved in the implementation of AI or those that are using AI, because now the stock prices are right/ if you can say AI, that I do AI, immediately you get a premium. I mean, for those big tech companies that are actually facilitating AI, of course, their stock prices has gone sky... Nvidia has been parabolic, right?
Martin Fleming: Absolutely.
Mark Zandi: You have this great incentive now. Moody's has a great incentive. It's good business, but it's also great for the stock price to really invest and to adopt and try to figure this thing out. Moody's is aggressively investing in it. Of course, we're kind of uniquely like Varicent situated here because we got a ton of data. It's hard to get to the data. It's hard to understand the data. It's hard to interpret the data, and AI, you layer that on top of this data that we have and go, oh, my gosh, there's a lot of things that come out this. That might accelerate the adoption, the change in the business model that will lead to bigger productivity gains. Just a thought. Maybe it's not 10 years down the road. Maybe it is over the next five years.
Martin Fleming: No. No. Absolutely. We're going to see this process roll out over a period of five, six, seven, eight years if it's going to be successful. By 2030, we're going to be executing business processes in fundamentally different ways. I would say it's not only those publicly held firms who are either going to benefit or feel pressure from the equity markets, but also competitive pressures. Even privately held firms are going to find that, unless they can respond in a competitive fashion, they're going to be losing significant market share and many will go away.
Moody's is a great example of an organization with enormous amount of text data from the various firms that have been assessed and evaluated, all of the SEC publicly held documents. There's enormous opportunity for natural language processing in financial services applications where all of the technology is being very aggressively and rapidly adopted and deployed.
Mark Zandi: It sounds like, it feels like, and I don't want to put words in your mouth, but I'm going to put them in your mouth and let's see if it tastes good or not, it sounds like you view this more positively, like we need the productivity growth, this is going to help give us the productivity growth that we need. You don't view this as dystopic, wiping out a boatload of jobs and people are going to be on mass unemployment or significant increase in unemployment. Do I have that right?
Martin Fleming: You do have that right. I certainly would not say that success is certain. There are probabilities, right? We're talking about the future. The future is uncertain, but I would say certainly, more likely than not, better than 50%. I would probably say there's a 70% chance, 60 to 70% chance, that in the two decades ahead, we, the US and developed nations across the Northern Hemisphere are certainly likely to have the opportunity to experience significantly stronger growth than has been the case over the previous two or three decades. Now, what does that mean for the labor market?
Mark Zandi: Go ahead.
Martin Fleming: There's been a great deal of work among our colleagues in the profession identifying occupations and tasks that can be performed by artificial intelligence. What we have been able to show again in our work at the MIT-IBM lab is that suitability for machine learning or the technical feasibility of machine learning does not mean it's economically viable. Computing can be very costly. Training AI models is very costly. Being able to accumulate the needed data is costly. We've done a great deal of work in computer vision, and we're just beginning to do the work in the language models. Just take computer vision, it turns out that only about 20% of the tasks that are suitable for computer vision capability are economically viable for computer vision capability because of the cost and the business case that needs to be made.
There are two problems. One is the fundamental economics. The business case is not there, and the second is making those business cases in the way that organizations behave creates a great deal of uncertainty and risks. CFOs are very reluctant to engage in these large projects because they really don't have the data around what, number one, the probability of success is and, number two, what the likely benefits will be. There's a relatively small proportion of these tasks that currently are economically viable for the use of artificial intelligence.
The 20% estimate, by the way, is roughly consistent with work that McKinsey has recently published. They have a number of 21%, so, technically, we're at 18%. They're at 21% roughly, plus or minus. They also say that over the course of a decade, with the improvement of technology, that will increase to 29%, so we're talking about 20 or 30% of tasks that are suitable for artificial intelligence are economically viable for artificial intelligence.
Mark Zandi: Great. I just want to tell you what we've done in our forecast, and I'd love to get your reaction to it. We're a bit more cautious, but just to provide context, and correct me if I'm wrong, but this is kind of the heuristic thought in my mind, between World War II and the financial crisis, non-farm business productivity growth in the United States was about almost 2% per annum on the nose. Between the financial crisis and up until the pandemic, it was closer to one to one and a half percent, closer to one in the immediate wake of the financial crisis, closer to one and a half percent by the time the pandemic hit.
Since the pandemic hit, it's been one and a half percent per annum, so we're about half a point down from where we were for much of the post-World War II period. We are assuming that productivity growth will accelerate for a number of reasons, but the primary reason is the increasing adoption and use of AI, and we get back to 2% essentially over the course of the next five years, so by the second half of this decade, we'll be back to about 2% per annum productivity growth, consistent with long run historical post-World War II lows.
What do you think? Is that a reasonable forecast in your mind?
Martin Fleming: It is reasonable.
Mark Zandi: I expect you to say yes, but go ahead.
Martin Fleming: I look at it a little differently in that I define the period between 1945 and 1975 as what we call the deployment period of the third industrial revolution. That's when the manufacturing and fossil fuel technology was mature, it was widely adopted in all of the kinds of related change and transformation. Think of the building of the highway network across North America. Think of the investment that was made in defense, in space expenditures that supported all of the activity.
A great, quite relevant issue is around the Treaty of Detroit. The Treaty of Detroit was when the UAW and the auto workers came to agreement in the late 1940s, early 1950s that allowed for a change in the work rules and, in turn, cost of living increases. Healthcare benefits and pension benefits were created, which is why we have the system in the US for private healthcare that we have today with the intervention of President Truman at the time. That was the Treaty of Detroit. That structure then spread to many other industries, the steel industry, the plastics industry, the chemical industry. It fundamentally changed how work was done. We had this period of 30 years where we had about two and a half percent productivity growth per year.
The technology then ran out of gas. We hit diminishing returns and growth, in fact, as you well know, was under such great pressure. We had a period of quite high inflation as a result, which got out of control. It's reasonable to think that if this transformation of this nature, which is quite significant, begins to occur, that we'll go through a period, as you point out, going from a percent and a half productivity growth to 2% productivity growth and, potentially, greater than 2% productivity growth if the extent of transformation is fully realized.
Mark Zandi: I love the optimism, but let's end the conversation with some darker thoughts. When I say that, I have in my mind's eye congressional testimony by the titans of the AI industry coming up to Capitol Hill, and tell me if I'm wrong, but they were pretty dark in their perspectives on what AI might mean. You hear words around existential humankind, there's a non-zero probability that this could eliminate humankind. I mean, come on. I mean, really dark stuff. What do you think about that conversation, and maybe you can quickly then pivot to, okay, what do we need to do from a policy perspective to make sure that that does not happen?
Martin Fleming: As your question suggests, it is a little overstated.
Mark Zandi: Okay, and you're a little understated, I would say.
Martin Fleming: The comments, I mean, are probably a little overstated. Look, there are risks. There clearly are risks. Think about nuclear power. If for example today in the US we had more widespread use of nuclear power, we'd be consuming significantly less fossil fuels. On the other hand, there are all kinds of issues around safety and the use of spent fuel as well, but it's been regulated over the years. We have the Nuclear Regulatory Commission. We have expertise in government agencies that helps to manage the risks to provide the benefits more broadly. That appears to be what we need to do today.
There is legislation that Senators Graham and Warren have proposed to address issues around privacy, online behavior, security, et cetera, which eventually will likely begin to appear. There are some significant issues that the Supreme Court has recently raised around the major questions doctrine of how specific does the legislation have to be before an agency can take specific action. There are a lot of issues to work out, but an agency that provides significant expertise to address these issues is likely to appear and, based on experience over the past 50 or 60 years, there's a probability that it can be successful.
Mark Zandi: Got it. Got it. Martin, you've been very kind with your time, and I know we're running out of time, but I do want to quickly turn to Cris and Marisa.
You obviously got a real expert here in the AI world. Did I miss any questions? Is there anything you were wondering about or perplexed about that you'd like to ask Martin? Maybe I'll go to you first, Marisa, because I can see the perplexed look on your face.
Marisa DiNatale: My mind is just spinning.
Mark Zandi: Spinning? Okay.
Marisa DiNatale: Yeah. Yeah. Martin, you think we're maybe, what, 10 years out from widespread adoption across all different types of businesses?
Martin Fleming: Yeah. These things are not necessarily that precise to say 10, but anywhere between seven and 12, I guess. There's a range.
Mark Zandi: That sounds pretty precise to me, seven to 12. Gee whiz, that's pretty good. Okay.
Marisa DiNatale: What has to happen to get there? Is it more around policy or is it the development cost of the technology?
Martin Fleming: It's all of the above, and that's what creates the uncertainty as to whether we can realize the benefits that we've seen in the past, in prior industrial revolutions.
Mark Zandi: Hey, Cris, anything on your...
Cris deRitis: Well, lots on my mind.
Mark Zandi: Lots of things, Yeah.
Cris deRitis: Clearly, many different directions to go here. I guess, I have a question on this last point about regulation. I've been thinking about this a bit. I find it difficult to actually believe we can regulate. You make the analogy to nuclear power or other things that are in the physical world. You do have some way to actually manage those, put in specific rules that forbid transport of certain goods, for example, but here we're talking about lines of code. If the US puts up a barrier, well, you just move your code somewhere else. I struggle to see how government itself will be able to regulate this. It seems much more of a free market.
Martin Fleming: Yeah, so let me give you an example. OpenAI, who are the developers of ChatGPT, has been involved now with a legal action that has been launched by Sarah Silverman. Folks may know her as a comedian. She's written a number of books, and she has objected to the use of her books, and other authors have joined her, as data for large language models. Now, the large language models love books because it's highly curated data. An author and an editor, maybe multiple editors, have gone through the text. Every character and punctuation mark has been precisely placed. That's the gold standard data that large language models need, but it's all protected by copyright.
Now, Google has a large library of books online whose copyright has expired, but that's an example of a place where the copyright law comes into play, that guides the nature of the data that are available for these large language models. There are many other issues, but that's one example of where effective legislative activity around the copyright law comes into play to regulate activity.
Cris deRitis: Okay. I'm hopeful.
Mark Zandi: You should know, Martin, that Cris always looks on the dark side. I have to say he's always-
Cris deRitis: I watch a lot of sci-fi movies.
Martin Fleming: Mark, one other point I would mention that we have not touched on and that you and I certainly are focused on is monetary policy. What does all this mean for monetary policy? If you're a Fed board member or a regional bank president, how should you be thinking about this?
I assert that there are a couple of concerns. One is it probably means higher interest rates for longer. There are lots of reasons to think that interest rates will be higher having to do with debt, particularly public sector debt, private sector debt, but if all of the build-out of this capability that I've been describing to you is to become real, there are enormous capital requirements. The data center investments that are occurring currently are quite substantial. Part of my work with the BEA is to help them measure data center investment more precisely. We don't do a good job of it today, but it's really looking like it's quite substantial. Those are all capital requirements that are going to have to be managed with higher interest rates, number one. Number two, it probably means that, as a percent of GDP, the Fed balance sheet is going to be somewhat larger.
We talk about the Fed balance sheet getting smaller in dollar terms, and that may happen, but over time it probably needs as a percent of GDP to be larger because of the capital requirements and, number three, it probably has implications both for R-star, the target, real rate of interest, as well as the Phillips Curve. The Phillips Curve probably will be much steeper as innovation occur, as markets become more competitive and prices are responding more rapidly to changes in cost. There are some significant implications from a monetary policy point of view that I know from discussions the folks around the board and support the board and the FOMC are only beginning to realize.
Mark Zandi: Yeah, it reminds me of the late 1990s when the internet was coming on the scene. Productivity growth was really strong. Inflation was starting to be suboptimal. It created all kinds of confusion as I recall before we could get our minds around it and before the monetary policy could react. It feels like this might be the same thing.
Martin Fleming: Exactly.
Mark Zandi: I do want to thank you and, Martin, one more time, what's the name of your book for folks out there?
Martin Fleming: Breakthrough, A Growth Revolution.
Mark Zandi: Growth Revolution. It sounds like a fantastic book. I'm going to go buy that as soon as we get off the podcast, but thank you so much for the opportunity to chat with you. I learned a lot in a very digestible way, so I really do appreciate it. Thank you.
Martin Fleming: Oh, it's been a lot of fun, and I'm pleased to have spent the time with you.
Mark Zandi: If you don't mind, we will knock on your door in the future.
Martin Fleming: Absolutely. Love to do it.
Mark Zandi: I just want to point out your forecast numbers seem to be serially correlated. Everything takes longer. That probably means the productivity revolution is likely next year. Just saying in a nice way.
To our dear listeners, thank you so much, and we will talk to you next week. Take care now.