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00:00And following straight on from Daniela, it is so fascinating to see how she thinks about already whether there's data
00:07showing the labor force displacement.
00:09She talked about it being overseas. She really talked about how we're thinking of purpose.
00:14So I give you the two choices, a choice of abundance and utopia or one of existential crisis and risk.
00:21Where does Mary Daly or where does the San Francisco Fed sit on on where the economy is, utopia or
00:27demise through AI?
00:29Well, like most things in the world, neither of the extremes are usually useful as good descriptors of where we're
00:35likely to end up.
00:36So I think ultimately the future that we create is our decision.
00:41Technology doesn't make choices. It's a tool and is a powerful tool.
00:45But we need to make the choice of where we want to be. And I think they touched on it
00:48in the last panel.
00:49Daniela talked about that, about how you have to think about what do you want this to do for you
00:54and what do you want a life that comes from it?
00:56And that's where we are. So I'm enthusiastic about what the models and tools can do.
01:01But I do believe strongly that we all need to appreciate what we need to do with it and how
01:08we need to help everyone come along.
01:10Otherwise, the world of the doomsayers versus the enthusiasts gets determined by things outside of us.
01:16And really, we want to harness it. I always tell people, you know, technology can harness you or you can
01:21harness technology.
01:21I think we should be in the driver's seat and not the passenger seat here.
01:25So when you're thinking about the driver's seat of of how can you get the closest data, whether it be
01:30macro, whether it be anecdotal.
01:32So what is actually happening? How people are harnessing it or not? Where are you turning to?
01:37I'm really turning to businesses and asking them, what are you doing?
01:42And not the technology businesses, the businesses that are going to determine whether this is as transformative as many think
01:49it will be,
01:50or whether it's just a cost effective play that makes you better at doing their past jobs,
01:56but you're not going to be creating new opportunities.
01:58And I'm already seeing very clear signs that businesses are asking, not the question of how can I use it
02:05to simply do things faster, better, cheaper,
02:08but how can I do things differently?
02:10What did I never think I could do in my business that now I have the capacity to consider?
02:16And one of the CEOs we talked to said, we've thought we were going to be thinking about costs, and
02:23now we're thinking about revenue.
02:25And I said, well, tell me more of what that means.
02:27He said, we thought we were a business that did this, and now we know we can be a business
02:31that does this.
02:32And we can bring our design.
02:34We don't have to go and get designs to do our machine tooling.
02:37We can build our designs because we can use model-assisted work,
02:41and we can change how we think about agriculture to make it have better yields and less disease
02:47and, importantly, bring prices down for consumers should all of that come through.
02:51So it's one of these things where I see people tackling the technology that they thought they needed to learn
02:57to keep up,
02:58and now they're thinking about how do I use it to grow.
03:01That's what we do.
03:02I just want to bring that chart back again because I don't know if the audience – and this is
03:06where Mary Daly is so fascinating
03:08because she reads the data, but she also wants to hear from you.
03:11So please send us your audience's questions.
03:14I want to be seeing them and integrating them with our conversation.
03:16But look at this.
03:17The adoption rate that we've seen by sector, of course, information, ITs out front, the professional services.
03:22Look at poor old government.
03:25But I'm sure in many ways that's because you're restricted.
03:28There are governance more in place on government use and adoption.
03:33How is the San Francisco Fed using AI in these tools?
03:36You know, the Federal Reserve has taken a system approach because we are part of a Federal Reserve system,
03:41and so we've really worked hard.
03:44All the way back when ChatGPT first came out, the first thing we have to do is say,
03:48you're not allowed to use these technologies to do your work because we're in a confidential environment
03:54and we have ring fences.
03:55I know all the private sector companies started thinking about that.
03:58But then we quickly realized that it's an important tool.
04:02For the whole time I've worked at the Fed, which is longer than I now talk about anymore,
04:06but really my whole career, it's really been about being not the first out there taking risks,
04:13but the next out there being thoughtful about how technologies could be used elsewhere
04:18because we have to study the economy, we have to know how financial institutions could use them.
04:22But we're also really committed to being good fiduciary stewards of public funds,
04:28so we want to be using technologies that can make work more efficient, more effective, more resilient.
04:33But we're recognizing constantly, even though you're right, we are regulated,
04:37but we're also self-regulating in this way.
04:40We are fiduciary stewards of public funds, but also fiduciary stewards of public trust.
04:45So we're always balancing how do we continue to modernize while we're making sure we're safe
04:50and we're sound and we're doing our work well with a human in the loop.
04:54That's what we always think about.
04:55Humans got to be in the loop.
04:56Well, then for us humans who are wondering how we bring it more into our personal life,
05:01into our professional lives, but how we bring others along,
05:04what are you seeing in adoption rates within those that you work with,
05:07not just those that you're going out and talking to?
05:09How are you seeing your own colleagues feeling empowered to use it or feeling totally distrustful of it?
05:15You know, well, we've been on this journey for a while.
05:18We, you know, all the way back starting in 2023 and thinking about this in San Francisco.
05:21You know, we've got lots of ambassadors for this, but on any growth curve or maturity curve,
05:28there's the early adopters, the enthusiasts.
05:30I'm probably one of those.
05:31I'm using all the models I can get on my private device, thinking about how I can do practice.
05:36You know, can I run my equations that I've written down much faster?
05:40Can I estimate models better?
05:42Can they code faster?
05:43All of this type of thing.
05:44But the important thing is we quickly built a sandbox in the Fed where we can do these things safely
05:51and people can practice.
05:52Well, that means that it's not just the leaders who are using it and saying you must use it
05:57or you should use it.
05:58It's actually ground up as well.
06:00And so there's the enthusiasts, the early adopters, and then you bring the others along.
06:05And I would say that we have, and I feel proud of this at the system level,
06:09we have taken this as it's the individual's responsibility to invest in him or herself,
06:16but it's also the organization's responsibility to make sure we're training the workplace
06:21that's ready for tomorrow, not just the workplace that's ready for yesterday.
06:25And so thinking about how we enterprise train people on these tools in a safe and sound way,
06:31making good judgments.
06:32It's also, you know, you don't need generative AI for every single task.
06:39We've long been using machine learning and robotic process automation.
06:42You can use older technologies like those, which seemed new and cool before.
06:48You can use those older technologies to do your work well.
06:51And so I think it's just a matter of helping people in our organization.
06:54We've got widespread adoption at this point, helping people understand that when you get
06:59down to it, it's not just about learning a tool.
07:02It's about seeing how it can benefit you.
07:04And one of the ways we think that we can change business processes is have people who are actually
07:09in the work innovate within that space, but do it in a safe and sound way by having a group
07:16of people take a look at that and say, do we want to take this to scale across the enterprise?
07:20Or is this just a one-off thing that was nice to experiment, but it's not going to give ROI?
07:25Well, ROI is so front and center, has been for the last couple of years.
07:29And still we're waiting for that tangible data that shows the productivity is really at an
07:36inflection point.
07:37And from your perspective, is it about the reskilling, the training from a government
07:41perspective, a public perspective, a private, me as an individual perspective that's holding
07:45us back?
07:45Why haven't we seen it in the data?
07:47Well, the very first thing to know is that there's, you know, this famous phrase that
07:52productivity growth is everywhere except in the data.
07:54The data itself at the aggregate level that we collect, that's a true thing that was said
07:58by Robert Solow.
08:00You know, it's just like this is important because it always happens.
08:03Where the productivity growth is starting to take place, the productivity gains.
08:07But in order for that to get to the aggregate level, it has to be across a wide group of
08:13individuals
08:13in firms.
08:14So we are seeing evidence in particular firms, in particular sectors, where you're already
08:19seeing those gains.
08:21But we haven't seen it at scale yet, in part because if you think back to, I'm going to
08:26use an historical example.
08:28They're not perfect guides, but I think it's useful.
08:30Think of electrification.
08:31We had electricity for a long time before we got the rapid productivity growth that came
08:37from electrification.
08:38The change wasn't that we knew how to use electricity.
08:41We had that.
08:42The change was that instead of just putting the electric motor at the end of a factory
08:47line that was once powered by steam, where you saved costs for energy, but the line looks
08:52identical, the unit drive came in and they could make machines with specific motors for
08:59themselves and they could rearrange the factory anytime they wanted.
09:02And then transformative things happen.
09:06What's the key?
09:07It's business process change that generates sustained productivity gains and firms are
09:12just at the early stages of interrogating, learning the technology, using the technology
09:17and then thinking, how do I change my business so it doesn't actually look the way it once
09:21did?
09:22For that, you need workforce, you need investment, you need learning.
09:25And then, of course, as Daniela mentioned, the models are changing so quickly, the capabilities
09:29that most of the firms we talk to don't want to just take one thing and say, that's
09:33are new because they know six months, one week from now, things could be totally different.
09:38I mean, what hasn't changed and has been relentless for a couple of years has just been the wall
09:43of money coming into the AI picks and shovels, the infrastructure build out.
09:47Is that showing up in an inflationary pressure perspective?
09:51We keep talking about bottlenecks, about memory prices.
09:53How is that affecting from a macro perspective?
09:55So, we haven't seen those particular things.
09:58Certainly, that's a concern.
09:59And if you go to particular places where data centers are being built, you can get information
10:04about how it's harder to get to construction workers because they're all being moved there
10:08or there's a demand for construction workers that might outstrip.
10:11But for the construction workers, it's a boom, right?
10:14This is a good thing.
10:15And, of course, we need more pipe fitters and welders and all the things that we thought
10:19for a long time we didn't need.
10:20Now, we know we do need them and community colleges in particular are training a lot more
10:25people to do those types of jobs.
10:27But we haven't seen that drive the inflation numbers that everyone's worried about.
10:31You know, the number one concern when we talk to people in communities, you can see it in
10:34the Gallup surveys, et cetera, is inflation.
10:36But inflation is really being driven by just the ongoing, we're trying to get inflation down
10:42to 2%, of course, but then we have the tariffs and those are rolling through and hopefully rolling
10:47off, you know, the effects of the tariffs rolling through and rolling off by the end
10:51of the year.
10:52But then we have the oil prices, which are pushing up overall energy costs and, of course,
10:56food prices as well.
10:57So those are the things people seem much more worried about than the data centers.
11:01It certainly has caused some supply gaps between the things that power electric plants, the
11:07chips, et cetera.
11:09But I don't think that's really the main driver in any way of inflation right now.
11:13But, of course, your mandate remains steady prices, it remains financial stability.
11:18And we've got a great audience question here saying, you know, how do you think in the
11:22longer term AI will affect your mandate?
11:25You know, I don't think of it as affecting our mandates.
11:28Our mandates are given to us by Congress.
11:30And so let me just say they're full employment and price stability.
11:33And those are always affected by how the economy grows, how fast it can grow, what the
11:40underlying aspects of it.
11:42And so what many people are wondering right now in that particular mandate, it calls,
11:47is in the next year, will AI itself affect our specific decisions that we're making as
11:54we navigate this?
11:55And I say, you always are thinking about what the potential of the economy is.
11:58But we also, if you're a policymaker, you have to think about what's really happening
12:03today.
12:03And there it's oil prices and other things.
12:05And then there could be this idea that maybe the job market won't be as robust as it's
12:10been in the past because AI will do so much.
12:13So far, we have seen mostly AI, generative AI, being used to augment workforce rather than
12:22replace workforce.
12:23Now, it is absolutely true that if you're going to take, if coding is easier, you won't
12:29need as many coders.
12:30But what we're hearing from firms that use coders is they're hiring new coders, new types
12:35of coders.
12:36So net hiring is going up or hasn't fallen very much.
12:39It's just the people's, the skills you need are different.
12:42And so the responsibility for all of us is to think, how do we provide training for people?
12:48So that we, and it's not going to be like any one component.
12:52You're going to have to have individuals saying, I want to invest in myself and learn these new
12:56skills, companies saying, well, let's, how, how do we mobilize our workforce by providing
13:00training?
13:01And the public sector asking, what do we need for our nation?
13:04And I think those three things together, individual, private and public together, thinking
13:09about what's our journey forward as we make sure this workforce is ready?
13:13And that anyone who used to do this job and now has to do these kinds of things can prepare
13:18themselves to do those.
13:20Dare I push us into the five, 10 year landscape and what we're hearing from your new Fed chair,
13:27Mr. Walsh, is that maybe it could be deflationary.
13:29It could be, absolutely.
13:31Are you abiding by that, you think?
13:32Yeah, I think, you know, one of the things that's true about technologies is when you invest
13:36in them, they, you know, that can, the investment comes first, right?
13:41You have to invest in the capital, the infrastructure, the technology itself, and then you invest in
13:47the people so that they're ready.
13:49And investments often compete for resources.
13:53That is the early part.
13:55Then the next stage is where do you get, start to get the gains?
13:58But if it is a, is transformative as some of the other technologies have been that we
14:03call transformative in our history, then you absolutely start seeing productivity gains.
14:08And that means the pie grows faster and we can, things can fall in value and fall in
14:15prices and inflation can, it can be deflationary.
14:18And the timing is really what matters.
14:20So, you know, you talked about the five and 10 year landscape.
14:23We're talking about the, you know, 12 month landscape when we're thinking about policy decisions
14:28we have to think about today.
14:30And then we're thinking about the five and 10 of where we're heading.
14:32And you've, you've go back to the periods in history where the Fed has had to grapple
14:36with these technology, technological changes.
14:39I started at the Fed in the mid nineties when we were grappling with how much would
14:43computerization, the internet, uh, create opportunities to hold prices back.
14:50And so those are the kinds of things that we are doing right now, but I don't think that's
14:53the pressing issue today.
14:55Today, you know, inflation's above target for different reasons, but going forward, we
14:59definitely have to think about this.
15:01In the news, unfortunately, we're thinking about the next day or the next week, this
15:04time next week, we know that SpaceX is going to be pricing.
15:09We've got a wall of like money desperately trying to come into these public companies.
15:15We've got assets at near record highs, if not at them.
15:18Is that ever a cause of concern for you or the Fed?
15:22You know what we, we certainly, the Federal Reserve system, you know, you can see that we
15:26release twice a year, our, our quantitative surveillance kind of summary.
15:30But, but essentially we're always watching financial stability issues, but we are asking
15:35these questions.
15:36And this is a question I ask.
15:38What is the value of the work being done behind that?
15:42And I think it's really hard to trace back and say that these, the technologies that people
15:47are investing in aren't valuable.
15:50We see them in your personal life.
15:52I'm sure all of you, if you're at a tech conference, are using AI, you know, for your personal life
15:57and your business, et cetera, just thinking about it.
16:00And the more you use it and the, this rate of change you see it making, the more you recognize
16:06there's a possibility.
16:07So I think there's some, there's there, there, right?
16:10Will there, is there potentially some, you know, change?
16:14I, I do not use, there's two words I don't use and I'm not saying them, but one of them
16:19is that word.
16:20Um, the other one, it starts with an R and so, but, but seriously, we don't talk about
16:27these things.
16:27No, in all seriousness, I think, I think that it's, you know, look, think about the conversations
16:34we're having.
16:34And a lot of times you see, you hear conversations about AI is going to save everything.
16:40Nothing else will ever be bad again because we have AI.
16:43And on the other side of it, you hear AI is going to destroy everything.
16:46You also hear that, oh my gosh, it's a B word.
16:49Oh my gosh, it could cause this word.
16:50Nobody will work.
16:51And I, I just would caution all of us from staying at these extremes because the hard
16:56work, the work that is going to determine what our future looks like with this technology
17:01is all in the middle.
17:02It has not, it is nowhere in their extremes.
17:04It's about thinking, okay, if we use this and these people don't do these jobs anymore,
17:10what do they do?
17:11And if you look back on history and you wonder why did technologies have a negative outcome
17:17on society when over time they had a positive outcome, electrification, it's often because
17:23the people who were being displaced didn't have an opportunity to grab another run.
17:28And I think it's really incumbent on, on all of us to think, how do you have, how do you
17:33enable opportunities so people can grab another run and how do you harness the technology so
17:39it can do work that we haven't been able to do or thinking we haven't been able to do,
17:44you know?
17:44And every week you open the, uh, the news and you see another thing that AI has done for
17:49us that has made our lives potentially better.
17:52So let's not lose sight of that while we also don't lose sight of what has to be done by
17:57all of us in order to make sure this is a positive rather than a negative experience.
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