00:10:42 · Grace Gong, Kevin B. Boyd
[00:10:42] S0: When you're thinking about budgeting out your, like, your... I don't I don't know if there's, a yearly budget or, like, if there's, like, a particular KPI for sales, like... But, like, you know, for... I guess, like, for, like, a regular or commercialized company, obviously, everybody have, like, some sort of, KPI and then, like, a budget to block for like each subsector. So when it comes to creating your own like yearly goal, how do you really like think about like whether it's like time or like budget allocation to spend on since like you know we talk about like cloud co work and like you know when you're making a decision to choose between cloud and like maybe this other like top competitor or like other solutions in the market. Obviously I could think of security as a huge thing and then maybe the budget stuff is another thing. I guess how do you typically... First of all, how do you... Is there, like, particular, like, road map that you're... You have for your own team? And then how do you select the solution that's presented to you? Mhmm. [00:11:54] S1: Well, so when you ask about budgets, you know, budgets are always a challenge at universities. You know, it feels like we never have enough. [00:12:02] S0: Never have enough. You know, that's everybody.
00:12:02 · Grace Gong, Kevin B. Boyd
[00:12:02] S0: Never have enough. You know, that's everybody. [00:12:06] S1: You know, that's that is everywhere. You know, so when we when we develop our budget each year, we're really looking across all the categories. So, you know, how much do we think is the right amount to invest in AI? How much do we think is the right amount to invest in information security? There's always making sure that you're modernizing applications, modernizing the infrastructure. You know, like, if you think about the, you know, university, we have hundreds of buildings. And all those buildings have infrastructure. And that infrastructure is, you know, constantly needing to be replaced, whether that's, you know, wired network or wireless and whatnot. So much of it is like having a plan. So how do we look out, you know, the next year, the next three years, the next ten years? What makes sense? And where are the things that we must do? You know, where if you don't do them, the wheels fall off the car. And then where are the things that want to invest because it makes the organization more efficient or more effective. So, you know, how do we make those decisions? It's it's a lot of conversation with our business partners about, you know, what are the challenges that they're facing? What are the are the things that we need to work together to help help them be more effective?
00:13:29 · Grace Gong
[00:13:29] S0: Thinking about, like, when you're choosing, like, I don't know if any startup come to pitch you all the time or like how do you kind of navigate that I'm sure like you know there's a lot like companies are probably trying to whether it's like sell or like distribute to you Chicago or like equivalent like when you're interacting with, I wouldn't say unproven, but like it's more of like an earlier stage company. How do you like, if you guys ever purchase a product off like existing, like smaller company before, how does that kind of like process? Basically, how do they typically sell to you that you felt like, oh, you're comfortable with trying a particular product?
00:14:14 · Kevin B. Boyd
[00:14:14] S1: Yeah, no, it's an interesting question. And I think there's actually a couple of answers. So one of the things to understand is that universities are not, at least our university in particular, is not very centralized. So there, you know, there's... We provide central IT and central infrastructure, But there's a lot that goes on in a very distributed fashion. Research labs, schools and divisions across the university. But what we do for our core central systems, generally don't do too much with startups. We do some, you know, if there are point solutions. But it's always this question of, okay, is it something that we can't get from anywhere else? And is the company still going to be there six months from now? And so, you know, think that's hard for us sometimes because what we're looking for is when we buy solutions, we want to make sure that they're still going to be here a year from now, two years from now, three years from now. Now, said that, there's a lot of innovative technology that gets integrated into our research all across the university. And I think there's a much more, much greater tolerance for things that, you know, it meets a need today. The need may be completely different a year from now.
00:15:35 · Grace Gong
[00:15:35] S0: So when, I guess like in the on the campus, is there any trends that in the AI space that you're really excited about whether it's coming out of like research or like, since like U Chicago has like a lot of like brilliant minds in terms of like, you know, I feel like a lot of students are probably innovating in the AI space as well. Is there any huge trends that you're personally seeing like a huge growth on campus?
00:16:00 · Kevin B. Boyd
[00:16:00] S1: You know, I guess the place that I'm the probably the most excited is it's what AI can really do for some of our research disciplines. And I'll give you a couple of examples. If you think about the research process, in multiple different disciplines, what you're often starting with is a problem. You have a problem that you're trying to solve. And it's a question of, all right, what's my hypothesis of how I'm actually going to be able to solve that? So in the biomedical space, which for us is very large, our biomedical sciences is about a third of the university, you have people who are researching how to address certain diseases. But if you think about the process for that, it's a lot of, okay, what are all the different drugs, all the different combinations of things that might have a positive effect on that same disease. In some ways the process is the same for the material sciences. So someone who's working on developing better batteries, is, you know, it's huge in the industry right now. So whether that is batteries that go into cell phones or batteries that go into cars. You know, there's a lot of research going on right now about, alright, how do we make a battery that holds more charge, charges faster, lasts longer, And much like in the biomedical sciences, you're trying to figure out, you know, what might have a positive effect on the disease in the same way in materials science, it's thinking about lots of different combinations of lithium and sodium and different chemicals that might produce a better battery. AI can be incredibly helpful in narrowing some of those things down, so that what you actually go and test in the lab are things that are more likely to be successful. So I think that's really exciting. Some of the other things though that have been just fascinating, where I think the start we might not have thought that AI would have a huge impact. So we have many individuals who research ancient languages. You think, well, what does AI have to do with ancient languages? Well, AI is really good at patterns and relationships. And in some of the ancient languages, you have certain languages that we think were a predecessor of certain other languages.