00:00We are talking a lot about AI. Tell us about your approach at Alliance Bernstein and how you're doing it
00:05in terms of the financial aspect and for clients, but also within the workflow.
00:10Sure, yeah. I think AI has had lots of different impacts across the organization, right? From
00:15investment performance to client engagement to operations, right? So the impact is broad.
00:20I have four pillars to think about this, right? First is making sure we identify the right
00:24opportunities, right? Having AI really impact those. But the second third pillar is just as
00:29important. Making sure I build the foundational capabilities to empower the organization,
00:34right? These are tools that the whole firm can use. Third pillar is about making sure they have
00:38the dexterity and the talent to actually execute on those tools, right? So that's the third pillar.
00:42The last one is really about governance, right? Making sure we do this in a responsible way. So
00:46those are the four pillars and that's how we execute AI across A.B. What's a bigger promise
00:51for AI? Is it productivity within an organization sort of reshaping how people work or is it actually
00:58finding alpha? Yes, certainly in the near term, it's productivity, right? That's about doing things
01:02faster, but eventually it's got to be investment alpha. My north star is investment alpha. How do you
01:09do that in an environment where everybody's using these same tools? So it's really about filtering the
01:14signal from the noise. As you likely know, very low signal to noise ratio in finance. So how do you
01:18use
01:18these tools to filter out that signal, isolate the noise out, right? So there are a few ways. We'll
01:24have all the same tools. Certain techniques, right? First is proprietary data. We have data no one else
01:29has. The way we make decisions, the data we've collected over time and the analysis that we've
01:34done over time, right? So proprietary data. Second is really prompting. How do you ask the prompt makes
01:39a difference, right? I'm sure you know that yourself, right? So the context is important. The last one,
01:43just as important is fine tuning. How do you tune these tools so that they align with our investment
01:49philosophy, our way of thinking, the way we do research, the way we manage portfolios, and hopefully that
01:54creates differentiated alpha going forward. Proprietary data. It does feel like ultimately
02:00that's going to be the advantage. So where is your proprietary? I mean, obviously it's proprietary, but what
02:06kind of data, like what is it that you guys are working with? It comes in many different forms. It
02:11could be the
02:12way that you interact with the markets that you collected over time. It could be the conversations
02:16you had with your customers, suppliers, and companies. It could be the way we've made decisions
02:21over time, right? It could be the research models that we've built over time, right? All of those
02:25constitute proprietary data. If I can then fine tune these tools to think the way that we have done
02:31over history, you know, that makes these tools hopefully a lot better and produce alpha the way
02:35we've done in the past. How is the team about embracing AI? Yeah, they've been fantastic, right?
02:40So everybody? Wow. I mean, there's always a spectrum, obviously, as you know, right? But
02:45the way that I think about this is the ones who are great, the great investors, by leveraging
02:50these tools, they're like awesome now, right? So it's really empowering your best investors
02:55and they see it, right? So when you see your colleagues doing awesome things, you know, getting
03:00investment alpha from these tools, they're like, yeah, I got to have some of that, right? So
03:03that's where we see this, lots of people are embracing it because they see their colleagues
03:09really excelling with these tools, right? Our colleague, Lisa Abramowitz, had a great
03:12interview with Jamie Dimon at JPMorgan Chase yesterday. And he said something that really
03:16stuck with me. He said that he was asked about AI and the opportunity. He said in like 30 or
03:2140 years,
03:21my guess is people are going to be working four hours, four days a week and living to 120. A
03:27lot of
03:27cancers will be cured. A lot of diseases will be cured. Food will be safer. Cars will be safer.
03:31It will be a wonderful thing. Do you agree? I think a lot of that can definitely happen.
03:37You think so? Yeah. I think it's, you know, I would like to think that I'll be still working
03:43eight hours, 10 hours or whatever it is, because we'll just put our energies in different things.
03:47Per week or per day? Oh, I guess per day. I'm still thinking per day. He's the same per week,
03:51right? But the safety side, I mean, I think those are the benefits, right? I think we still have to
03:58worry about the governance side, right? How do we make sure that we grow these tools in a safe and
04:02responsible way that within finance and society, you know, we, we, uh, we're able to leverage these
04:07tools in a right way. Well, Andrew, when you laid out your pillars, was it four of them? Yes.
04:11Four of them. Governance was at the end. Yes. How come? Why is it not number one?
04:15Well, I view all four pillars as, as equally important. They support the foundation that we're
04:20on. So I just happened to name that. So you're moving in terms of governance. You're figuring it out
04:25as you guys go. Governance has been like a critical part of how we think about this,
04:29right? It starts from the risk appetite your firm wants to take, right? Is your organization
04:32comfortable with the risk that these tools introduce? What does that mean? What type of
04:36governance you have? I feel like governance actually, uh, creates more innovation because
04:41once you know the rules, you know, the sandbox you're able to play in, right? I feel like our
04:45employees are able to innovate at a speed that's faster than they normally would.
04:50I hesitate to end with this question, but does your workforce becomes
04:55smaller as a result of it? I think over time, you know, what, what, what these tools
04:59do is really augment the tasks that they have today, right? I actually like to use the iron
05:03person in analogy. I don't know if you've heard of this before, but the iron person is, is as you
05:08know, a smart subject matter expert inside. So that means I still need the smart subject matter expert
05:14and analyst and accountant. It doesn't really matter what it is. But the reason I like the iron
05:17person analogy, that that's the first thing. The second one is it's clear who is accountable
05:21for the decision. It's the human. So that's why from, from, from my perspective, this is really
05:26like augmentation, growing the capabilities, giving them more breadth and depth. Humans have
05:32fat fingers. Humans make bad trades. It's true, but so do these, these, uh, these tools,
05:37but hopefully by combining the two, we can make better investment decisions, right?
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