B (3:18)

I think that's excellent advice.

A (3:20)

Jeff, how do you see AI changing the skills we'll need at Morgan Stanley and the way that people should think about their careers?

C (3:28)

I think you have to break this down into three pieces and Stephen sort of alluded to it. One, you have to look at the jobs that are likely to disappear. Two, you have to look at the jobs that are going to change. And then finally you have to look at the new jobs that are going to actually emerge from this phenomena. You should be thinking right now about how you are going to prepare yourself with the right skills around learning how to prompt and learning how to move into those functions that are not going to be eliminated. In terms of jobs that are changing, they're going to require a far, far greater sense of collaboration, creativity. And again, prompting prompt engineering is sort of the center of that. And I would highly encourage everybody, every single person who's listening to this, to become the single best prompt engineer in their group, in their friend, in their organization. And then in terms of the jobs that are created, I'm actually pretty optimistic here. As we build agents, there's actually a bull case that we're going to create so much complexity in our environment that we're going to need more people to help manage that. But the skills are not going to be repetitive, linear skills. They're going to require real time decision making, leadership skills, collaboration skills. But again, I would go back to every single person, learn how to talk to the machine, learn how to be creative and practice every day your engagement with this technology.

A (5:01)

So then how are companies balancing the reskilling with the inevitable culture shifts that come with any new paradigm?

C (5:08)

So, so first of all, I think if you think about this as a tool, you've already lost the plot. I think that, number one, you have to remind yourself what your strategy is, whatever that strategy is, this is an enabler of your strategy. The second point I'd make is that you have to go from both the top down in terms of leadership messaging, that this changes here. It's important and it needs to be embraced. And then it's a bottoms up because you have to empower people with the right tools and the technology to transform their own work. Because if you're trying to tell people that this is the path that they have to follow, you don't get the buy in that you need. You really want to empower people to leverage these tools. And what excites me most is when people walk into my office and say, hey Jeff, let me show you what I built today. And it could be some 22 year old who it's their first month on the job. And what's exciting about this technology is you do not need a technology background. You need to be smart, you need to be creative. And if you've got those skills, you can build things that are really innovative. And I think that's what's exciting. So if you can combine the top down that this is important and the bottoms up with giving people the skills and the technology and the motivation, that's the secret sauce.

A (6:34)

Jeff, what's your advice for the next generation college students, recent college graduates, as they're thinking about navigating the early parts of their career in this environment?

C (6:45)

Well, Katie, first of all, I agree with what you say. Everyone's like, what should I study? And the answer is, I don't actually know the answer to that question, but I would study what you care about. I would do something that you're passionate. And the second point, and I hate to be a broken record on this, but I would be the single best user of generative AI at your college. Volunteer with some nonprofit, build a use case with your friends. When you walk into your first job, impress in your interview that you are able to use this technology in really effective ways because that will make a difference in your first job.

A (7:25)

And I'm curious, are there areas where you think humans will always beat AI, whether it's in financial services or other industries?

C (7:34)

I like to think that we are human and that gives us the ability to build trust and emotional relationships. And I think not only are we going to be better at that than machines are, but I think that's something that we as humans will always want. I think that there may be some individuals in the society that may feel differently, but I think as a general rule, the human to human relationship is something that's really important and I like to think that it will be a differentiator for a long time to come. So, Katie, from where you sit as the head of Global Research. How has Genai changed the way research.

A (8:12)

Is being done with the help of your team? Jeff, we have now embedded AI through the life cycle of investigating a hypothesis, doing the analysis, writing the research in a concise, effective way, pushing that through our publishing process, developing digital content in our analyst voice in the local language of the client. And now we're working on a client engagement tool that helps direct our research team's time. And, and so the impact here is it reduces the time to market, to get an alpha generating idea to our clients and, you know, and it's freeing up time for our teams.

B (9:00)

So Katie, I want to build on that. Productivity is a big theme and away from the research itself. From a management perspective, how are you and your team using AI and what do you see as the benefits and how are you spending the extra time that's freed up by AI?

A (9:14)

I like to say that the research AI strategy is less about the tools. I mean, those are critical and foundational, but it's more about how we're evolving workflow and how our teams are spending time. And so the savings are being reinvested in actually your area thematic research, which takes a lot more coordination, collaboration, a global cross asset view. It just takes more time to develop and test a hypothesis and debate internally and get those reports to market. But it's critical for our core strategy, which is to help our clients generate alpha. When you look at equity markets over the past 30 years, a very small number of stocks drive all of the alpha and they tend to link to themes. And so we're reinvesting time in identifying those themes earlier than the market to allow our clients to capture that alpha. And then the other piece is when we look at our analyst teams, they spend about a quarter of their time with clients because they have to meet with experts in the industry. They need to do the analysis, they have to build the financial forecast, manage their teams. We have internal activities, build culture, and with the ability to leverage these tools to speed up some of those tasks, we think we can double the amount of time that our analysts are spending with clients. And if we're putting thought provoking, often thematic, global, collaborative content into the market, our clients want to spend more time with us. And so that's the ultimate impact on a personal level. And I think both of you can relate. I think a lot of the freed up time right now is just following the fast pace of, of change in AI and keeping up with the latest technology, the latest vendors. But long term, my hope is that this frees up time for more human activities on a personal level, learning the arts, staying active. So this could be potentially very beneficial to society if we reinvest that time in both productive activities that have impact in in business, but also productive, rewarding activities outside of the office. As we wrap up, it's clear that the influence of AI is expanding rapidly, not just in digital and knowledge based sectors, but increasingly intangible real world applications. As these innovations unfold, the way we interact with both technology and our environments will continue to evolve both on the job and elsewhere in our lives. Jeff Steven, thank you both for sharing your insights and to our listeners. Thank you for joining us. If you enjoy the show, please leave us a review wherever you listen and share thoughts on the market with a friend and colleague today.

B (12:22)

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