And I'm following straight on from Daniela. It is so fascinating to see how she thinks about already whether there's data showing the labor force displacement. She talked about it being overseas, really talked about how we're thinking of purpose. So I give you the two choices: a choice of abundance and utopia, or one of existential crisis and risk. Where does Mary Daly or where does the San Francisco Fed sit on where the economy is—utopia or demise through AI?
Well, like most things in the world, neither of the extremes are usually useful as good descriptors of where we're likely to end up. So I think ultimately the future that we create is our decision. Technology doesn't make choices. It's a tool and is a powerful tool. But we need to make the choice of where we want to be, and I think they touched on it in the last panel. Daniela talked about that, about how you have to think about what do you want this to do for you and what do you want in life that comes from it? And that's where we are. So I'm enthusiastic about what the models and tools can do. But I do believe strongly that we all need to appreciate what we need to do with it, and how we need to help everyone come along. Otherwise, the world of the doomsayers versus the enthusiasts gets determined by things outside of us, and really, we want to harness it. I always tell people, technology can harness you or you can harness technology. I think we should be in the driver's seat and not the passenger seat here.
So when you're thinking about the driver's seat of how can you get the closest data, whether it be macro, whether it be anecdotal—so what is actually happening, how people are harnessing it or not—what are you turning to?
I'm really turning to businesses and asking them, what are you doing? And not the technology businesses, the businesses that are going to determine whether this is as transformative as many think it will be, or whether it's just a cost-effective play that makes you better at doing the jobs, but you're not going to be creating new opportunities. And I'm already seeing very clear signs that businesses are asking not the question of how can I use it to simply do things faster, better, cheaper, but how can I do things differently? What did I never think I could do in my business that now I have the capacity to consider? And one of the CEOs we talked to said, we thought we were going to be thinking about costs, and now we're thinking about revenue. And I said, well, tell me more of what that means. He said, we thought we were a business that did this, and now we know we can be a business that does this and we can bring our design. We don't have to go and get designs to do our machine tooling. We can build our designs because we can use model-assisted work, and we can change how we think about agriculture to make it have better yields and less disease, and importantly, bring prices down for consumers should all of that come through. So it's one of these things where I see people tackling the technology that they thought they needed to learn to keep up, and now they're thinking about how do I use it to grow.
That's what we do. I love it. I just want to bring that chart back again because I did it with the audience. And this is where Mary is so fascinating because she reads the data. She also wants to hear from you. So please send us your questions. I want the questions. I want to be seeing them and integrating them with our conversation. But look at this. The adoption rate that we've seen by sector—of course, information, it's out front. The professional services, look at plural government. But I'm sure in many ways that's because you're restricted. There is the governance more in place on government use and adoption. How is that the San Francisco Fed using AI in these tools?
You know, the Federal Reserve is taking a system approach because we are part of a Federal Reserve system. And so we really worked hard all the way back when ChatGPT first came out. The first thing we have to do is say, you're not allowed to use these technologies to do your work because we're in a confidential environment and we have ring fences. I know all the private sector companies started thinking about that, but then we quickly realized that it's an important tool. For the tool time I've worked at the Fed, which is longer than I now talk about anymore, but really my whole career it's really been about being not the first out there taking risks, but the next out there being thoughtful about how technologies could be used elsewhere because we have to study the economy, we have to know how financial institutions could use them. But we're also really committed to being good fiduciary stewards of public funds. So we want to be using technologies that can make work more efficient, more effective, more resilient. But we're recognizing constantly, even though you're right, we are regulated, but we are also self-regulating in this way. We are fiduciary stewards of public funds, but also fiduciary stewards of public trust. So we're always balancing how do we continue to modernize while we're making sure we're safe and we're sound and we're doing our work well with a human in the loop. That's what we always think about. Humans got to be in the loop.
Well, then, for us humans who are wondering how we bring it more into our personal life, into our professional lives, but how we bring others along, what are you seeing in adoption rates within those that you work with, not just those that you're going out and talking to? How are you seeing your own colleagues feeling empowered to use it, or feeling totally distrustful of it?
You know, well, we've been on this journey for a while. We, all the way back, starting in 2023 and thinking about this in San Francisco, we've got lots of ambassadors for this. But on any growth curve or maturity curve, there's the early adopters, the enthusiasts. I'm probably one of those. I'm using all the models I can get on my private device thinking about how I can do practice, you know, can I run my equations that I've written down much faster? Can I estimate models better, can I code faster, all of this type of thing. But the important thing is we quickly built a sandbox in the Fed where we can do these things safely and people can practice well. That means that it's not just the leaders who are using it and saying you must use it or you should use it. It's actually ground up as well. And so there's the enthusiasts, the early adopters, and then you bring the others along. And I would say that we have, and I feel proud of this at the system level, we have taken this as it's the individual's responsibility to invest in him or herself, but it's also the organization's responsibility to make sure we're training the workplace that's ready for tomorrow, not just the workplace that's ready for yesterday. And so thinking about how we enterprise train people on these tools in a safe and sound way, making good judgment. It's also, you don't need generative AI for every single task. We've long been using machine learning and robotic process automation. You could use older technologies like those which seemed new and cool before. You can use those older technologies to do your work well. And so I think it's just a matter of helping people in our organization. We've got widespread adoption at this point, helping people understand that when you get down to it, it's not just about learning a tool, it's about seeing how it can benefit you. And one of the ways we think that we can change business processes is have people who are actually in the work innovate within that space, but do it in a safe and sound way by having a group of people take a look at that and say, do we want to take this to scale across the enterprise, or is this just a one-off thing? That was nice to experiment, but it's not going to give ROI.
Well, ROI is so front and center, has been for the last couple of years, and still we're waiting for that tangible data that shows that productivity is really at an inflection point. And from your perspective, is it about the reskilling, the training from a government perspective, a public perspective, a private me as an individual perspective that's holding us back? Why haven't we seen it in the data?
