Chapter 1: What is the main topic discussed in this episode?
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To qualify, you must apply at apple.co slash getdailycash. Apple Card issued by Goldman Sachs Bank USA Salt Lake City branch. Offer may not be available elsewhere. Terms and limitations apply. Bloomberg Audio Studios. Podcasts, radio, news. Good morning, everyone. I'd hope that we'd start by talking about productivity and some of the data that you've just said, well, no one really knows.
But the question with AI in the U.S. economy is what has happened thus far, right? And so I'll hit you with some of the official data that I've been tracking. You can tell me the utility of it or not. Okay, I'm ready. In US productivity, I always go with the Bureau of Labor Statistics measure of output per hour x non-farm business sector.
And if you look at the data over 50 years, that chart was really interesting, the side by side of electricity and AI. Over 50 years, the average quarterly reading is about 1.9% annual rate on productivity. But something's happened in the last 10 quarters where it's higher. Yes. You know, just under 3%, 2.7%. Do we really know what that is? And is it AI?
We don't know. I mean, that's the part that makes it hard is in productivity numbers, especially when they're happening in what I would think of as real time, it's very challenging to assess or draw it back to exactly what the factors are that have shaped it. You know, in fact, people still don't agree on what happened in the 90s all the time, if you look at research.
So it's just something to keep in mind. So then what you do, what any good economist or person, any industry person would do, is they'd say, well, what am I seeing? What am I seeing?
And so right now, while we can't find it in the macro studies that would do very sophisticated empirical econometrics and ask the questions, how much of this is AI, we still can see that there's something going on there. The question isn't, is it happening? The question is, how long will it persist? And so clearly, something's happening in the economy.
But if you make a series, to go back to your question about productivity, if you make a series of one-time adjustments, so say you automate a production line, or you use AI to help in loan application process. You save money once.
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Chapter 2: What is the current impact of AI on the US economy?
You don't save money forever. I mean, you keep saving that money, but you don't get growth out of that. You don't get productivity growth. You get one-time adjustments to the level of productivity of your employees or your process.
So what we're looking for is a technology to give us consistently good changes in productivity so that all industries at scale get better, industries figure out new ways to generate revenue, new ways to do product design. new ideas to come and shape the economy. That's the thing that has a sustained productivity growth part. So it's undeniable productivity growth has gone up.
What's not as clear is how long will that last.
Broadly, people want to see and understand how AI impacts workforce. And more recently, maybe inflation. So if we go back to the 90s and what Greenspan saw in productivity gains contributing to economic growth, there was a consideration around both of those things.
Absolutely.
You said that it's not the playbook to go back to what happened in the 90s and apply today, but what do you see in those things? Is it possible that AI is driving productivity gains resulting in economic growth, but without the inflation?
It is absolutely possible and something we have to interrogate. I mean, right now, as you know too well, inflation is still above our target, our 2% target, and price level's been high, much higher for a long time, and people are feeling stretched by the high inflation that they see.
And now, oftentimes, people say, well, now AI is going to hurt the labor market, and so now I'm in double doom, as people say. But I think, ultimately, the way many people think about AI is the investment part of any technology can actually boost demand for goods, services, and people and can then raise the pressure on inflation.
But then the productivity part comes, and that's a disinflationary part. You can see this is all about the timing. And so what we end up investigating is not just the models, but asking questions. Are the build-out of data centers raising prices for construction workers? Are the build-out of data centers raising prices for metals and other things that go into them, the raw materials? Are the
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Chapter 3: How does Mary Daly assess productivity in relation to AI?
Very recently, the CEO of PG&E, Patty Poppy, came on the program and made the argument that It's possible that the data center build out within PG&E's jurisdiction actually brings down wholesale electricity prices because the hyperscalers take on the capital burden. And they are buyers in aggregate of electricity.
But many people, your constituents in the 12th district, will find it hard to see that argument playing out.
Well, I think we have to separate what we're talking about into now, next, later. So let's think about now. Right now, we have more demand than we have supply for energy, for electricity, right? If you talk to, we regularly have CEO roundtables with the big power companies across the 12th District. You can look throughout the nation. Demand for power is higher than the supply of power.
And there's a lot of reasons why supply is falling behind. One is demand's just gone up rapidly. But another part is that they've got an aging infrastructure. They have to get those things built out. It's a highly regulated industry, so the infrastructure doesn't just come on like a light switch.
Then you have, there were supply chain issues that made it hard to get the transformers and other things. So all of this just adds to the imbalance of demand versus supply. But the remedy for that isn't to take away demand, it's to increase supply. So when, you know, they talk about any CEO of a power company says, we can solve this problem by adding more supply. Absolutely.
But that's a next and later. And so what you said, my constituents, what consumers and businesses are saying is, I'm worried my electricity prices are going to rise, and they've already been going up. And the CEOs of power companies are saying, but if we just keep building, that will go down. And both are true.
Go down as far as it will be disinflationary.
Alan, you know, it's hard to say energy could be disinflationary if we get to a point where supply is greater than demand. Right now, I'm just looking for supply to equal demand. And that would be a big benefit to consumers because it would mean that we wouldn't keep seeing inflationary pressure coming out of the energy sector.
The other thing I wanted to ask you through the lens of constituents of the 12th District is one reason you might focus on productivity is there is a direct read-through to GDP growth and other data sets that you can look at. But the anxiety in the real world is, well, an AI tool can make me more productive or it can displace me altogether. Where do you see that tension in the economy right now?
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Chapter 4: What challenges exist in measuring AI's impact on productivity?
So if your job has certain tasks in it, AI can do those for you. And the next part is augmentation. So you have replace, augment, and create. What's interesting about AI is that unlike, say, electricity, when the candle lighters or the lamp lighters or the candle makers got displaced before the US completely became electrified. This is going more quickly.
If you go to a firm, I was on a panel at the Reagan National Library Economic Forum with Patrick Collison from Sprint. I'm Sprint. Oh, gosh, Stripe. Sorry. He's going to kill me. Stripe. Don't tell him, OK? But from Stripe. And interestingly, he said, I am hiring more coders than I'm laying off. But I am laying off coders whose technology skills didn't advance or they weren't the right workers.
And you're seeing this, right? You're seeing businesses re-skill themselves to meet the AI moment. And that's going to cause worker anxiety. And right now, worker anxiety is high. We're in a low-firing, low-hiring environment that's already going to make people feel vulnerable. If you haven't found a job and you're newly minted out of college,
you just think, I was supposed to get a job before I graduated. Now I still don't have one. That's very worrisome to people. And then if you're thinking, well, I might lose my job, but I don't know how long it will take to get another one, then you're worried about that. So I think it's natural for the sentiment to feel nervous.
But it's not the same thing as AI is taking all the jobs, because what we're really seeing is AI is replacing tasks. augmenting workers. When we talk to firms, most of those firms are saying, I'm augmenting my workforce. If you're in big manufacturing firms, they don't have enough workers that do skilled labor. And so they're looking to augment their workforce.
And then we're also seeing jobs created. It's interesting. I gave a talk on this in 2023. and I used prompt engineers as the jobs they were creating, but those jobs are now being replaced. That's a warning, right? That time period of change. It's a warning. You could think of it that way. Or it's an indicator. So let's take the warning. The warning is you can't keep up.
I would say let's use it as an indicator. It's an indicator that technology is evolving really fast and workforce is need to skill in durable skills. And durable skills are be AI ready, be able to use AI to lift yourself in the educational space.
Use the technologies that are out there to build your skills up, because you can learn a lot fast if you train yourself to look at AI, say, give me a lesson on how to write, I've been thinking about this, how to write a smart contract from end to end. What sort of software would I need? How would I do it? What would the code look like? How would I test the code?
How would I know it's right before I execute on this smart contract? And so you can do these things in an evening. And then it's just about being able to do that. So I think that's the message for workers. And I would have taught my young self this same thing, is if you put off technology because you're afraid of it, then you won't be in the first place.
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Chapter 5: How is AI influencing the labor market and job creation?
If AI is so good, can it process larger sets of data and make more accurate economic forecasts than traditional Fed models can?
Well, you know, we don't right now use AI in our monetary policy work. But we do use it in research. As researchers, if you go to any academic institution, you're going to see researchers using AI to see what they can do better on coding and other things, but also data analytics. What do you see? The place we are there is AI doesn't give you answers to problems.
It helps you get to the discovery perspective. So if I use AI as a researcher to look at a bunch of data, I still have to test my hypothesis. I have to go in with a hypothesis. What am I trying to answer? So that's the human person. And so that's why it's not particularly well tooled right now to replace our forecasters and our thinkers, our scholars who think about this.
It can't produce a more accurate neutral rate, for example.
You're still going to get estimates that are between 11 and negative 3 on the neutral rate of interest. I'm not kidding. The models that we have can produce an estimate from negative 3 to positive 11. What does that tell you? The neutral rate of interest is not a truth with a capital T. It's an estimate.
It's a theoretical construct to help us understand how to benchmark policy, but you can't use it as a threshold that you can do surgical adjustments around. No one calibrates monetary policy surgically with a neutral rate of interest estimate for those reasons. So where we are using AI though at the Fed, and many people may be surprised about that, would you like to learn about that?
Yes, please, how? I know many might think I work in an institution that waits for, we're still getting electricity. You might think that, but no. We actually are not the earliest adopters, because remember, we're fiduciary stewards of public funds, but also as fiduciary stewards of public trust.
And so we really have to make sure that we're working in the most risk-free and risk-managed environment we possibly can. But we have been at this since really in 2023.
So the first thing that we did as a system, and I'll really speak about the 12 Federal Reserve Banks that are across the country, we worked as a system to say, well, we need to make sure our employees, our teams, are ready to understand AI. So what do we need to do? We need to have lessons, work gatherings, et cetera, get people familiar and get people focused in areas that we can practice with.
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Chapter 6: What role does inflation play in the conversation about AI?
Most of the people who work with us are operations people. We process cash. We do all the electronic payment system backbones, make sure they operate on time. If you're in the financial sector, you know Fedwire, ACH, FedNow. All of that is operated by our operations teams. We also support Vice Chair Bowman in supervision.
of banks and all of those things are, and then we have all of our support people who help make sure that that occurs. All of that can be easily, if you can do AI and you can use it, you can think of opportunities. So the next thing we did, get our workforce ready is number one.
Next thing you do is, and this is again, like all the businesses we talked to, you see what your vendors are already offering, right? If you have a technology vendor, an accounting vendor, an HR vendor, And then you just turn the service on. But if you turn the service on before your team is ready, you don't really get an ROI out of it.
So again, fiduciary stewards of public funds, we have to make sure. So we're all still in this ring-fenced proprietary environment because the public has to know that we're not introducing risk to this. And then, of course, the place we're working now is where many, many people are working, which is if you have a lot of technology workers, then the coding assist is just so important.
And one of the things, back to workforce, it doesn't create a massive change in who you have working for you. What it means is they can do their work faster, better, and more effectively. And if you think of the three principles of the Fed, we need to be efficient, effective, and resilient. So it also builds in that resilience for us, because we have quality assurance.
unit testing, all the things that can slow you down if you're not right or interrupt your ability to serve, those things can all be assisted with AI in a really positive way.
So again, not monetary policy, but definitely, like all other companies who are working on this space, making sure we're not behind in delivering good value for, I mean, the shareholders of the Fed are the American people, and we owe them the effort to make sure we're modernizing ourselves and keeping up with the things that can help us do our work faster, better, and more effectively.
I do have two questions relating to AI and monetary policy quickly, and I know we want to get to some audience questions as well, because I'm conscious there are students in the room who will go out into the workforce.
I think the main thing, reflecting back on the 90s, is that there are anticipated impacts from AI on the economy, and PCE is the preferred gauge of inflation running higher, beyond 2%. How do you manage that? Many would argue that those anticipated AI-driven productivity gains would justify lower rates, but they are that anticipated.
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