00:00:01 · Grace Gong, Kevin B. Boyd
[00:00:01] S0: We are live. Hi, Kevin. Welcome to Venture with Grace. [00:00:06] S1: Thanks, Grace. It's great to see you. Ad[00:00:08] S0: I'm so excited about our conversation. Before we start off our show, want to give a quick shout out to our amazing sponsor. This episode is brought to you by Nebius, the ultimate cloud for AI innovators that these provides infrastructure you can come down combining reliability and speed with flexibility and engineering support on edge by hyperscalers AI leaders like meta Shopify and Higgsfield already partnered with nebulous to run their AI workloads, Plus Venture backed startup can save up to a 150 k on compute cost when applied for access. Visit navias.com or navias.com.com/startup to learn more. Okay, Kevin, I wanna start with your journey because, like, I feel like you are such an interesting... You have such an interesting life as like, I feel like when we met, I think you were like the first CIO of university I met IRL. So I wanna start with your journey because obviously you were working in the industry from like United airline to like, Chicago tribute to like, a lot of the actual, like great companies. And then you were the CIO of like New Chicago. And then now you are, you know, back to well, technically you are like there the whole time and then you were faculty of like Northwestern. As like a person went to school in the Midwest, I want to talk about like, you know, what is... Maybe we could start from like a day in your life as a CIO of UChicago. Like, what does that entail and how do you kind of like spend your time?
00:01:36 · Kevin B. Boyd
[00:01:36] S1: Absolutely. So, you know, I think one of the reasons I love my job is that there is no day that's the same. You know, we cover a lot of ground. So I think probably the most interesting part of the job is the work that we do to support research. University of Chicago is one of the top research universities in the world. And a lot of the time that we spend is thinking about how do we better enable and support the amazing research that goes on. And that could be biomedical sciences, it could be physics, computer science, social sciences. And, you know, I don't think it'll be any surprise to you that these days it's a lot of conversations about AI and how we can effectively use AI to support the research that's going on. One of the things that we did this summer is we rolled out Anthropics Claude with chat, co work, and code for all of our students, faculty, and staff. Excuse me. And that's been really exciting to see how that is being used. It's also led to many conversations across campus about what are appropriate ways to use AI, what are less appropriate ways to use AI, whether that's in classroom, in research and administration. And so that's been really interesting the last few months.
00:03:07 · Grace Gong
[00:03:07] S0: I I think like from our conversation, I feel like you guys have also are like also from like our research, you guys also have built out your own like internal system as well. Like, I think like we talk about, like, essentially, like, I remember when we're chatting something about quantum and as well as like the data center and stuff like that. Maybe we could talk about, like, you know, I know that, like, you know, rolling out like cloud or like a lot of the, like, I guess like AI native AI tools is extremely important. What are something that you felt like set University of Chicago apart from the rest of the universities from the AI perspective?
00:03:49 · Kevin B. Boyd
[00:03:49] S1: I think from the AI perspective, we've been taking a very thoughtful approach to it. So our president, Paul Alivisatos, wrote an opinion piece in, think it was the New York Times recently, and it was really talking about our philosophy of AI being with, without, and about. And what that means is there are many things that we want to do at the university with AI. At the same time, we've also been very intentional, particularly in the instructional side, of saying there are some places that we want to have teaching and learning happen without AI. And so the university has gotten a fair amount of press in the last couple of months about our humanities division, which has largely said, we want classes to be technology free now because we actually want people to learn. Know, something that's been key for us for many years is University of Chicago is not a place where you go to just memorize facts and regurgitate them. It's a place where you really learn how to think, learn how to challenge. And particularly as an undergraduate in the humanities and some of the other programs as well, the law school has been very public about like the first year law students. They want to be pretty much technology free. So it's this idea that we want you to learn. We want you to grow as a student before you start using AI to do everything that you're doing. And I think that's really useful. And then the about piece is we're gonna teach people about AI, how to use AI effectively, whether that's things like co working code, or it could be how to build agents and other tools like that.
00:05:52 · Grace Gong, Kevin B. Boyd
[00:05:52] S0: When... I guess, like, when you're thinking about designing on, like, a commercial AI... APIs, like, like, whether it's, a open source model or, a model that running your own, like, infrastructure, how do you typically make these kind of decisions? [00:06:07] S1: Sure. I think in many ways there's sort of the old way of doing things and then there's the new way of doing things. Universities are generally pretty conservative. So historically, they don't change all that fast. Technology wise, you know, our focus has been on how do we make sure that things are just rock solid, stable, and you know, delivering what our faculty, staff, and students expect from us. You know, in an AI world, we're really having to be more innovative, move faster, build tools that maybe in the past we would have focused more on buying things. Now we're actually building them just because it allows us to deliver efficiency and effectiveness faster. So I think when you talk about, you know, how do we make decisions about AI or about, you know, the APIs and things like that, It's really a question of how do we both get to market fast, deliver the business value that our business partners across campus are looking for, but at the same time make sure that it's going to deliver the same stability and reliability that they're looking for.
00:06:07 · Kevin B. Boyd, Grace Gong
[00:06:07] S1: Sure. I think in many ways there's sort of the old way of doing things and then there's the new way of doing things. Universities are generally pretty conservative. So historically, they don't change all that fast. Technology wise, you know, our focus has been on how do we make sure that things are just rock solid, stable, and you know, delivering what our faculty, staff, and students expect from us. You know, in an AI world, we're really having to be more innovative, move faster, build tools that maybe in the past we would have focused more on buying things. Now we're actually building them just because it allows us to deliver efficiency and effectiveness faster. So I think when you talk about, you know, how do we make decisions about AI or about, you know, the APIs and things like that, It's really a question of how do we both get to market fast, deliver the business value that our business partners across campus are looking for, but at the same time make sure that it's going to deliver the same stability and reliability that they're looking for. [00:07:31] S0: When you were thinking about, I guess, would you say is the most important skill like a CIO needs to deliver like a leading AI adoption across like different organizations and technical teams of like, you know, and like researchers and faculties and administrative. Seems like they all like have like different priorities. How do you kind of like think about the AI adoption from that lens?