00:38:12 · Kevin B. Boyd, Grace Gong
[00:38:12] S1: So again, it depends. Like the very large gift that just came from the Schmidt Foundation, That one is intended for science. Thank you. And... But it's available. It's going to be available very broadly across the universe... Across the university. When a researcher goes and let's say they get several million dollars in grant funding from NSF, NIH, or one of these types of agencies, that is very specific to like whatever the project is. So basically what they will do is they'll say, I want to research XYZ, and here's what I need in order to do that. You know, this computer hardware and memory and storage and some money to administer it. That can really then only in most cases be used for what the the grant was specifically written for. [00:39:19] S0: Mhmm. So when... I guess, like, when you're thinking about, like, since, like, you know, you were... You since... You started working at U Chicago for... Since, like, 2012 compared to like from in the past like ten years or so like what would you say is like the core differences the school has changed and like you know nowadays like we're in like the AI era compared to like past like technical trends, whether it's like blockchain or like, you know, other different trends, like what would you say is like the core differences that in terms of like impact of the school? [00:39:57] S1: Yeah. Know, AI is different. I think in some ways AI is more similar to what we saw around ninety nine, two thousand with the internet becoming, you know, more widely available. [00:40:15] S0: And
00:40:15 · Grace Gong, Kevin B. Boyd
[00:40:15] S0: And [00:40:16] S1: the reason I say it's more similar to that than anything like blockchain, you know, I think if you really look at it, the impact of blockchain was, you know, limited to relatively specific areas. Whereas if you look at the impact of ubiquitous internet, you know, basically access to all the world's information all the time, First from a computer and then ultimately from a mobile device in your pocket. That's tremendously transformative. AI, I think is the same kind of thing where most organizations want to move really fast. And I think what we're going to see over the next few years is AI just embedded in everything. And you know, much like what we saw twenty six years ago, at the time everyone was talking about the internet and what you could do with the internet. We don't spend a lot of time talking about that anymore. Like the internet's just, you know, part of everything that we do. I think AI will be the same thing where now we're, you know, we're being very intentional about, well, you using AI to create agents? Are you using AI to do this? You know, is AI good or bad? I think we'll get a few years down the road and, you know, AI will just be in the things that we do. We'll stop talking about it. It will just be, all right, what's the business problem we're trying to solve? And how do we use all of the technology that's available to us to do that? [00:41:50] S0: One of the audience is asking, what's your perspective on going cloud versus on prem with your technology around campus?
00:41:50 · Grace Gong, Kevin B. Boyd
[00:41:50] S0: One of the audience is asking, what's your perspective on going cloud versus on prem with your technology around campus? [00:42:01] S1: Yeah. No, it's a great question. So our strategy over the last, let's say seven years has been very much with our applications, moving them to the cloud. So in the vast majority of cases, that's software as a service. So, you know, when you look at our core systems, it's Workday for HR, it's Oracle Fusion for our financial applications, Our admission systems are Slate, which is a SaaS based. Canvas is the learning management system, which is SaaS. Our advancement systems or alumni relations systems are built on the Salesforce platform. So that's been a very clear direction for us, and I think that served us well. It's given us a lot of really useful capabilities. We, you know, our very our intentional strategy is to move off of on premises for administrative applications. And we're trying to do that as rapidly as we possibly can. But I make the distinction with research because that's that's not so much the direction with with research. Research, you know, as I said, because of the nature of the compute requirements, it often tips the scale to actually doing things on premises where you buy the hardware and then you have that hardware available to you for, you know, for the life of the the equipment.
00:42:01 · Kevin B. Boyd, Grace Gong
[00:42:01] S1: Yeah. No, it's a great question. So our strategy over the last, let's say seven years has been very much with our applications, moving them to the cloud. So in the vast majority of cases, that's software as a service. So, you know, when you look at our core systems, it's Workday for HR, it's Oracle Fusion for our financial applications, Our admission systems are Slate, which is a SaaS based. Canvas is the learning management system, which is SaaS. Our advancement systems or alumni relations systems are built on the Salesforce platform. So that's been a very clear direction for us, and I think that served us well. It's given us a lot of really useful capabilities. We, you know, our very our intentional strategy is to move off of on premises for administrative applications. And we're trying to do that as rapidly as we possibly can. But I make the distinction with research because that's that's not so much the direction with with research. Research, you know, as I said, because of the nature of the compute requirements, it often tips the scale to actually doing things on premises where you buy the hardware and then you have that hardware available to you for, you know, for the life of the the equipment. [00:43:31] S0: What's your thought on... No problem. And then also, hi, Samuel. I wanna say hi to the audience who comments. So when you're thinking about, like, your current AI stack for your software, for your team, what does that look like?
00:43:31 · Grace Gong, Kevin B. Boyd
[00:43:31] S0: What's your thought on... No problem. And then also, hi, Samuel. I wanna say hi to the audience who comments. So when you're thinking about, like, your current AI stack for your software, for your team, what does that look like? [00:43:52] S1: Does that look like? So... Yeah. Wait. So are you talking about, like, when we're building agents or you're talking about other things? [00:44:03] S0: When you are working internally. And then the separate question would be like, you know, what's your thought on AI agents? So... Because right now, currently, personally, I'm using, like, Instinct and then you mentioned about, like, cloud co work. Right? So, like, does every faculty is building out their own agent? And like I also had a question and before I like, know, I feel like I have like a very limited memory, but when you're thinking about like, I feel like nowadays like everyone's using let's call it like Claude or try GPT to ask questions or get answers. How does school actually prevent people from like, I guess nowadays, like everybody is using it anyway. So like, do people still do homework that's like without AI or like people just default that this person will use AI, but we'll just grade people based off the AI answers.
00:44:53 · Kevin B. Boyd
[00:44:53] S1: Okay, so you got a lot of questions in there. So let me try and tackle them sort of one by one. So if you think about AI more broadly across the university, like I can answer, you know, what we're doing with our central systems. But you know, as I say, we're very distributed. So there's a lot of other technology that available within, in particular research, teaching as well. So, you know, as I've said, like, we've gone big with the Anthropic, making that available to everyone. Previously, as you mentioned, we had built our own own AI tool. It was called Phoenix AI. It was using the OpenAI models, but we put our own front end on it to, at the time, provide the level of security, privacy, and digital accessibility that we wanted. But the reality is that we also have every one of the frontier models is in use in different ways, whether that's Google's Gemini, OpenAI's ChatGPT, the Meta models, and then open source models as well, or open weight models. It really depends on the application. And there are some things where it's high sensitivity. Like, I'll use my information security team as an example. They tend to use more of the open weight models running on prem because they really don't want the highly sensitive security data going to the cloud. There are other things where, in some cases, faculty may just be experimenting with different models. In other cases, are particular models that are better for particular disciplines. And I think that's something we'll increasingly see in the future, where, you know, if you think of the frontier models, many of them are... The corpus of information is very broad. But what's really useful in, let's say, biomedical sciences and the legal field and some of these other fields are models that have been... Where the corpus of information has been more carefully curated. The legal field is a great example of this. You don't necessarily want a model providing answers to an attorney that has, I don't know, you know, some random information from the internet that... Because the corpus of knowledge came from Wikipedia or something like that. So I think that's true. Now, you had a question about students and homework. You know, that's a challenging question. And we, in so many ways, we don't view that as a technology problem. We view that as a people challenge.