AI & Sustainability

In this episode of The Prompt Report, host Dave Humphreys sits down with Mike Weinstein, Director of Sustainability at SNHU, to explore the environmental impact of AI. They discuss how AI data centers are driving a surge in electricity demand — with the Department of Energy estimating usage could reach over 10% of U.S. electricity by 2028 — and the associated water consumption from cooling processes. Mike breaks down the concept of Scope 1, 2, and 3 emissions, explains what SNHU is doing through partnerships like Sustainable IT and transparent ESG reporting, and shares how the university is using AI itself to improve campus energy management. The conversation wraps up with practical tips for everyday users, including prompt minimalism, mindful device usage, and staying informed about clean energy policy — emphasizing that the real power lies in collective, data-driven action.

Transcript

SPEAKER_00 0:05

Hey everybody and welcome to this week's episode of The Prompt Report. I'm here with Mike Weinstein, a director of sustainability at SNHU. Mike, thank you so much for joining us today.

SPEAKER_01 0:17

Yeah, Dave, thank you so much for having me. I'm excited to uh talk with you about this.

SPEAKER_00 0:21

Yeah, sustainability and AI is one of the probably biggest questions we get on the day-to-day. And so it's really great to have you on to talk a little bit about uh the concerns that we're seeing, some of the work that's being done around the university and the larger, bigger picture of how sustainability fits into the larger AI conversation. Yeah, absolutely. So to kick it off, what do you see as the biggest environmental impacts of AI that people should understand?

SPEAKER_01 0:56

Well, I think that the biggest environmental impact and the biggest story about AI is really about its electricity consumption. This is gonna remain something that I think is gonna be at the forefront of our understanding of the environmental impacts of AI. So AI consumes electricity because it's essentially just a bunch of computers, right, strung together processing information. In the same way that you've got to plug your laptop into a wall or your desktop into a wall to get some electricity flowing into it, whether you're storing it in a battery or not, you got to get some power into that machine before it can do anything. The same thing happens with AI, right? We're just talking about many more computers than your home computer. And so what this actually means is in terms of a measured effect, the Department of Energy estimated that in 2023, AI data centers used about 4.5% of the electricity generated in the United States. They are still predicting that within the next couple years that that could grow anywhere from double that to up to 12% of US electricity consumption. And so they're saying that by 2028, right, we'd be looking at more than, you know, probably more than 10% of all of our electricity going to AI. Widespread adoption and deployment of AI, right? That's going to keep going. I was just reading a report from Goldman Sachs that said, you know, they were looking at electricity pricing, which by the way rose like 7% last year, which is far above some of the estimates built into a lot of calculations of, you know, 2% to 4%. So a huge increase in demand for electricity, driving up costs. And their estimate was that almost half is that more than 40% of the growth in that electricity demand was coming from AI and AI data centers. So that's really kind of the environmental story. And if we understand what the impacts of electricity generation are, then we understand the impact of AI in context with the rest of our sort of modern global telecommunications infrastructure. If 100% of our electricity was renewable, if it was non-carbon emitting, endlessly renewable energy, then we could be telling a different story about the environmental impacts of AI. That's unfortunately not where we're at. We have made pretty significant growth. So about 25% of the United States electricity grid is renewable at this point. So that's pretty good. About a quarter of our energy is coming from renewable sources. That includes things like solar power, it includes things like wind power, also includes things like hydropower. About 20% of our electricity grid is nuclear, which is not renewable, but it does not emit carbon. So it doesn't contribute to global warming and climate change. But what that doesn't take into account is still the growth in electricity demand, right? So even as the percentages of our grid mix might be changing, right, the demand is still increasing. The US consumed, I think, like you know, 4 million kilowatt hours, you know, from last year, our emissions still went up. I had seen a recent estimate that, you know, that's about, you know, the United States released the electricity grid, about six billion tons of carbon dioxide into the atmosphere in 2025, right? So even with renewable energy making up, you know, a non-insignificant portion of our grid, right, it's still a lot of fossil fuel-fired power that we're generating. And is is unfortunately the case for a lot of where our AI data centers are located, they're located in areas where those grids are predominantly fossil fuel powered. So places like Virginia, which I believe has or Pennsylvania, those areas have some of the highest concentrations of data centers. Again, those grids are powered primarily by natural gas turbines. So that's oil that's you know removed via like the fracking process. So again, even where data centers are located doesn't really tell the whole story. So really electricity demand is the environmental story of AI and the environmental impact of AI. And along with that goes water. Water is something that we hear about. Water is definitely used in the cooling processes for AI data centers. About 70% of the water use that we're talking about for AI in general is from the electricity generation, is from the industrial processes of generating electricity. So that is the water usage primarily goes hand in hand with the electricity generation. Again, the more we move ourselves away from fossil fuel power plants, which require tremendous amounts of water to run and to cool, then we're going to continue to see increased water usage. Now, there is, of course, water used for cooling inside the data centers. It is primarily what's known as evaporative cooling. In that case, the the water is basically the cooling process comes from the water evaporating. And because the primary source of water for cooling in data centers comes from municipal water systems, what is essentially happening is that the data centers are moving the water out of whatever reservoir they're coming from, whether it's surface water is like a lake, a reservoir, or groundwater and aquifer sources, they're essentially moving that water basically into the atmosphere. So what we're talking about is moving water resources away from the local community. But the good news there is that there are a bunch of advancements in cooling, right? That things like closed loop systems where the water uh is not evaporated out and other and other methods, uh, methods of cooling that that don't rely on liquid cooling, but that's the most efficient and and primary use.

SPEAKER_00 6:33

That's really fascinating. How does AI electricity and water use compare to other consumer products? So I'm thinking like maybe like Teens chats, streaming Netflix, streaming Spotify, that type of thing that many people do day to day without thinking about the environmental ramifications necessarily.

SPEAKER_01 6:55

Yeah, that's a really great question. I had seen estimates of say streaming an hour of Netflix can release as much CO2 as you're driving uh a quarter mile in a passenger vehicle. So the interesting thing about AI, like you mentioned, right, is that it's it's just kind of a an acceleration from an existing trend or issue, really, which is that our our global telecommunications network that we rely on for so many things, but which is essentially sort of powering our economy at this point, or the the matrix that our economy is embedded into, yeah, requires constant power, power draw and requires constant electricity generation. So while I don't think I have a good answer on what specifically is the impact of, say, sending one chat GPT query versus streaming a half hour on Hulu, right? My guess is that that one query, right, however many tokens were sent to perform that AI query is probably less than a half hour of streaming, right? So sometimes I think those things are it's tough to compare apples to oranges. I do think that the point is that they're all questions of scale, right? So if it was just me and you watching Netflix and using AI, it wouldn't be an issue, right? But the fact that there is hundreds of millions of billions of people using this technology means that they're all questions of scale. And it's, I think it what it really requires us to think about is how are we powering this grid? Because no matter what we're doing, whether generative AI is on the table or not, what we've been using the internet for since it existed requires electricity and requires that electricity to be generated somehow. And so I think it really points to the need to consider our information technology sort of infrastructure within a more sustainable context.

SPEAKER_00 8:39

I really appreciate that framing. Thank you. You actually queue up the next question pretty well with the end of that answer, which is what are we doing at SNHU to maybe better understand or reduce the environmental impact, uh, environmental footprint of these AI tools?

SPEAKER_01 8:58

Yeah, it's really fun because it's sort of something that I've been working on since I started in this role and since I've been fortunate enough to stand up sustainability as a sort of function of the university, which is that from a point of view of a sustainability person, right, when we're looking at carbon emissions, right? It's it's kind of easy for us to look at the environmental impact of what someone is doing when they're driving a car around, right? Or when we're turning on a natural gas compressor to heat and cool a building. It's a lot harder for us to understand the environmental impact of something that is kind of hidden, like our telecommunications and IT network. And so as SNHU, that's sort of, at least for the past 20 years or so, has been our sort of unique challenge, which is we can take a look at the environmental impact of the emissions that are generated from our campus and from our mill yard and any other satellite facilities and the electricity that's generated on behalf of those. And that's relatively easy to measure, right? Once you start figuring out how to get the data, it's relatively easy to measure. And we've been fortunate enough to be producing uh emissions reports since regularly since 2021, but starting in 2018. The the real challenge in measuring those emissions is what's called scope three emissions. And this is the the greenhouse gas protocol's categorization. While the first two scopes, scope one and scope two emissions, are basically everything that I just talked about, right? Our fleet vehicles for campus, right? The electricity demand for our milliard building. Those scope three emissions are everything that is sort of in our value chain. So everything from our IT vendors to our cloud services to where we're purchasing from, to what we're mailing to students, to how we commute and how our staff fly around. All of those are included in those scope three emissions. And scope three emissions are the hardest to track because they require such broad collaboration and transparency from so many people. It's always been a real interest of mine to figure out how we accurately represent that at SNOO because those that that's really part of our story. Those scope one and scope two emissions for for our university really only represent a small percentage of our students and our people, right? Those scope three emissions, while they're the hardest to understand and measure, right, represent most of our people here at SNOO. So it's a really important story for us to understand and reduce the environmental footprint for our entire ITS infrastructure, including our AI usage. So again, AI represents an acceleration or a shift. But I do think in some ways that it's welcome to me because there's a lot more people out there suddenly interested in understanding and mitigating the harmful environmental impacts of a broadly distributed infrastructure. So, in terms of what we're doing, a good example of that is we recently joined up with the group Sustainable IT, which is composed of primarily private sector, although there's a few of us from higher education in there, and represents a pretty broad coalition of corporations and organizations, both on this side of the Atlantic and in Europe. And is really about the understanding of how IT contributes to sustainability as well as how we make IT itself more sustainable. And so it's it's from that group have come a lot of good things that we've already started to use. A part of that was an adoption of transparent ESG or environmental, social, and governance reporting. And while we've done ESG reporting for many years here in the form of things like STARS, which is our sustainability tracking assessment and rating systems report, which we publish annually, the idea with a sustainable IT ESG framework is that the things that people are asking about, right, how many, what what is the scope of our emissions coming from these data centers? What is the power demand for our AI usage? What is our governance for AI look like here? With the adoption of the sustainable IT ESG reporting, the hope is that we're going to get to those transparent metrics so that people are able to see, right? Our students, our alumni, our staff, our faculty are better able to understand what the impact is here at SNHU. And the real goal with that reporting isn't just to be transparent and just to lead to broad cross-sector collaboration so that everyone can reduce scope three emissions. But the also the alternative goal to that, or I should say the additional goal, is leading to data for us. And then if we have data, we can start to make decisions about how we reduce that environmental footprint. So beyond who we're collaborating with, if again, because sustainability is baked into our AI governance, right? And we decide particular tool makes sense for us because of how they view ESG and sustainability in their organization, we still understand that there are environmental impacts of using the technology. So by getting better data, which is where we're at currently, we're going to be able to understand how we offset those environmental impacts. And those offsets beyond collaboration, those are really the mechanism for scope three reduction. The question of those offsets is essentially you know, what are the offsets that make sense for Southern New Hampshire University? And the carbon offset market certainly has a lot of issues. It certainly has seen its share of, I don't want to say scandals, but certainly uh phenomena that have called into question a lot of the reliability and the credibility of some of these carbon offset mechanisms. They're questions that we can be having or conversations we can be having once we have the data. And so, where are we investing our money? We've already had conversations in the university about does it make sense to invest in renewable electricity for some of the communities in the places that we teach and learn. I think ideas like that are what's gonna come from us getting a better understanding of the scope of our emissions. And again, that understanding really requires transparency from the people that we partner with, right? From our vendors, from our cloud IT suppliers and services. The better collaboration we have and the more transparency we have from them, right? The better data that we get and the more decisions we're able to make that are gonna help us offset. So, in the meantime, we're deploying some solutions. I think we'll talk about some of those in a little bit, like prompt efficiency or prompt minimalism. We're deploying some solutions here internally. I'm still super interested in hearing more about small language models. I've done some like some looking into that on my end, but I want to see some of those rolling out. Those things seem pretty cool, right? The idea that we could run AI tasks locally on your CPU without having to send that out to the internet, right, to draw power from these data centers seems like for certain tasks a great solution for lowering the energy intensity and the carbon footprint of AI. And we're also trying to employ AI to solve some of our own environmental challenges at the university. So right now we're we're starting to employ AI in some energy modeling and energy management for our campus. And so the hope there is that as we are seeing energy efficiency increases here, right? That that is a in some ways is that allows us to think about an offset to our AI usage and that it's we're seeing real reductions in the amount of electricity that we're purchasing and energy that we're using here on SNOW, hopefully by employing some of these technologies.

SPEAKER_00 16:16

Awesome. Do you think, or how do you think, rather, you should balance the benefits of AI usage? So things like increased productivity, more tailored learning support, et cetera, et cetera, with the environmental cost.

SPEAKER_01 16:30

I actually really like this question. It's kind of a question to me about like a lot of the facets of the modern university and how AI is sort of used across the enterprise. When I think about learning support, for example, and tailored learning support for our students, I think about even some of our early work with Penny or chatbot. And I guess the questions, the answers to some of those questions might have to come from the students on the other end, right? And from the learning support teams, right? Were students seeing better support using these? Are they seeing, are we seeing the things that we want to see finishing their courses and their degrees here? Are they having an easier time navigating uh the systems and accessing education because of this learner support stuff that might incorporate AI solutions? If that's the case, right, then maybe there's a good reason for us to say, hey, now we need to balance that against the environmental costs. So I think learning support is a really interesting place of looking at yeah, what are the benefits? Like we need to actually get those. And I'm sure that the learning support team has a lot of that info. So that would be a really interesting conversation, and I would love to hear more from them. AI is already well embedded into the functions of a modern information technology arm of an organization. So, you know, now the question is, is you know, is that required like, you know, to have a modern functioning IT function? I can't really speak to that, but I know that it seems like really embedded in there already. Again, the good news is that just going back a little bit to the sustainable IT group, IT in general, and and our ITS team here at SNU in particular has a real vested interest in reducing our environmental footprint. And as such, right, the IT team, I think remains like a key champion in understanding the sustainability of AI. And so I'm really happy to partner with them here. And I think it really points the way towards how that should be functioning, not just at SNOW, but across higher ed and even, I would just say, the modern corporate landscape. So, how is that balanced? It's really tough to say, but that's something that they have an interest in understanding. I think about using AI from my point of view, right, which is I'm sort of using it for productivity's sake, right? If I'm having it tailor something that I'm writing or rework a spreadsheet that I needed to rework. I guess the real question is productivity versus time. I'm not at the point yet with anyone on my team, my amazing team, of expecting more from them because I know that AI can represent a vast reduction in the amount of time that they need to complete a task that includes generating something. I haven't moved on to that yet, but what's tough to say, is that what will be expected again in the future in a modern AI-empowered workforce? I mean, potentially in the same way that if I hand wrote a report, I would imagine that my boss would probably be interested in why I wasn't just using a word processor. So again, I don't know if we're there yet, but I think that we're maybe headed that way. So how is that balanced? I'm not sure. It's tough to tell. And I think that the last thing that's interesting is our teaching and learning, using these AI tools and teaching and learning on them. And I guess it's a real question of what responsibility do we have as educators if we believe that our students are going off into an AI-enabled workforce. Are we doing them a disservice if we're not including AI tools in their teaching and learning? I I think an understanding about the landscape of the future of work is really contextual here. So yeah, we're talking about all these different benefits or uses of AI, right, that need to be balanced against all these other things, right? Against what we're teaching students, against what we're expecting from employees, to, you know, the environmental costs of it. And I would say that in any case, right, as is the case with modern sustainability, I think that the balance needs to be, if nothing else, it needs to be transparent and it needs to be data driven, right? So that any member of this organization, right, can understand what the balance is. And then they can decide whether they agree with that balance or not. But I really believe that like that only occurs when it's transparent, when it's driven by data, and when it's really collaborative.

SPEAKER_00 20:35

I I really appreciate that this one thing that you keep coming back to is this push and pull of AI and sustainability. And there are benefits, certainly benefits to sustainability, and we're seeing more and more benefits to AI, right? But there's certainly downsides too. If we only focus on sustainability, then we lose the benefits of AI. If we only focus on AI, then we have more sustainability issues that pop up long term. And I so I really like the way that you're framing that, how it's it's about our own personal risk, it's about our risk as an institution, and that sort of tightrope, I suppose, that's between them, and how we navigate that and how we make sure that we're doing the best for the people and for the environment and for our learners, and right uh and that's hard work, one way or the other. You are very much on that tightrope.

SPEAKER_01 21:28

I think they're all kind of decisions that we we sort of make in our own lives, right, all the time. You know, and yeah, we have to have some sort of code, I imagine, to kind of guide us on what our decisions are that we're making and what the trade-offs are that we're making for any decision. But yeah, ultimately, and we'll probably get to this a little bit more later, right? But ultimately, like, you know, existing as a human being, you know, exacts some some small toll on the environment. You know, really, I I think the question is, you know, again, we're not just talking about me or not just talking about you or one person. We're talking about the scale of this, right? We've got 8 billion people here. Southern New Hampshire University has 200,000 plus of those people, right? That we have some responsibility for. And so it really is a question of all of us as a university kind of figuring that out, right? Like there's no such thing really as like Southern New Hampshire University, right? It is just the people who happen to be here at this time who make up this organization, both as employees and as learners. And I think that that that sort of tightrope walking is something that can be made easier when we're sort of all coming together to collaborate.

SPEAKER_00 22:33

Yeah, I love that. I love that idea of community and it's not any one person or one group that's driving change or impact. Instead, it's like you said a moment ago, informed data-driven decisions so that we can point at something and say, here is the rationale behind the choice that we're making in this case. And I think that's yeah, at least for me, helps me to gives me a little bit more confidence, I suppose, in a time of, I think, great uncertainty in many ways. But thinking about just the future of the planet and also the future of the these technology systems, right? It helps me helps me to process that, knowing that as an institution, we are coming together not as one person saying this is the way forward, but as a group and saying collectively these decisions need to be made and we need to understand them. Yeah.

SPEAKER_01 23:27

Yeah, uh agreed. Yeah, I think a lot of this from my point of view and my background coming from the environmental field, I I've had this sort of ongoing conversation right throughout my career with friends and colleagues and coworkers, right, about sort of the responsibility, the level of individual responsibility when it comes to the environment, right? The planet. Again, I I don't know. I ultimately I think that that's something that I can always have my mind changed on. What I do feel strongly about is that one thing is very clear, which is that at this point in time, right, it is, and potentially this is a universal truth, but I don't want to jump to that. But certainly now it isn't one person, right, who does all the damage, just as it isn't one person who cleans it all up, right? And the power really sits in the power of people as a collective, right? So whatever that collective organization of people is, right, whether it's a government or a business or a university, to me, that is sort of where the real power comes from, right? Because then we are talking about sort of those impact at scale and those sort of voices at scale and the actions of those voices at scale. So I do find that empowering. And I do think that there's really to me, that's kind of like the goal of modern corporate sustainability is the realization that all of these organizations, all of these institutions, it's really just us. And so can we use the powers of these institutions to help shift us towards a cleaner future, a more just future, and a more equitable future?

SPEAKER_00 25:00

And I think that we're doing that. One of the things that we've seen, conversations that I've had, people tend to approach AI use as an all or nothing. Like this doesn't feel right to me, a concern to say, since we're on the topic of sustainability, right? I'm concerned about the environment, so I'm not going to use the tool. Um, and that's certainly a decision that many people make. Totally fair. What about those people who are curious about it or who do see the practical benefits of it, but those concerns too? So, like that, again, that push and pull. What are some things in your perspective, tips or tricks you might offer those people to help make more grounded decisions around their use or create practical habits in their AI use to better align with their sustainability concerns?

SPEAKER_01 25:45

Yeah, absolutely. That's a great question. Yeah, because again, it seems like AI use is it at least becoming some sort of prerequisite for a modern workplace. Again, like that that's not a given, but it certainly seems like something that we will continue to interface with. So if it's something that someone is interested in using and exploring, right, and they're having those concerns, much like I am, there are some habits that I'd recommend that I try to follow as well. And I think, you know, the first of those is really just to consider the necessity of the AI task that you are about to perform. Maybe not even just do you use AI for this or not, but also like to what extent is the use, right? Do you, you know, are you requiring the AI tool to do all of your work in a particular case, or do you require it to maybe do 50% and then you can do the other half? I would really consider the necessity of what it is, right? Because we say this jokingly, right? But if the idea is that what you want to do is sit there and generate hundreds of hours of video slop content for Facebook to like fool our parents into thinking there's there's something going on, right? Like that's probably not a necessary use of AI. So assuming that's not what we're talking about here, you're you're probably already in a better position and from there just continue that sort of thoughtfulness. The other thing, the the other habit is particularly around prompting. And I've seen it, I've seen it called like prompt minimalism or prompt efficiency. And I know that your team has an entire framework on that. So I don't want to, I don't want to get that wrong. But I I do I do know that when we're focusing on those prompts, right, every time we're sending a query to you know a an AI agent, right, that's representing a data transfer from my computer to a data center somewhere where that data center has to then generate an answer and send it back to me. So if I know that there's electricity and there's power consumption used in that process, then I want to make sure that my prompts are precise and clear. A lot of times we're we're used to interacting with AI agents cordially and saying please and thank you and that sort of thing, which isn't necessary. So we can take those, we we can take a lot of that stuff out and we can really focus on on crafting precise, clear prompts. And I've also seen the ideas of sort of chaining logic, right, to avoid redundancy, meaning rather than me try and get the agent to do a five-step process all at once that I've got to go back through and fix up and send a bunch more prompts to get it right. If instead I have it do that five-step process one step at a time, right? Now I don't have to go back and tell it to fix mistakes that it made or that that wasn't exactly what I'm looking for. So I understand that efficient prompting once again is about just reducing the amount of demand that you're placing on the electricity grid. So following on from that is also to remember like your own power draw, right? Again, like we're tapped into this information technology network, regardless of whether you're using AI or not, right? So make sure that the devices that you're using, right, your monitors, your laptops, your desktops, make sure that they're energy star compliant, right? That's a government program, right, that baselines energy efficiency for devices. So make sure that it's energy star compliant so that it's not a needlessly wasteful device. Stuff that we've actually been talking about for years and years, which is still the case, right? Unplug your device if you're done with it for the day. Again, I've seen more recent estimates, right, that phantom power can be at five to 10% of a home's power draw. That means electricity that's being used for electronics that aren't themselves being used. So, again, pretty huge impact when we're talking about scale, right? When we're talking about millions of people, right? If we're remembering, right, to unplug our computers, right? Believe it or not, big energy impact. And I'd say the other habit that, you know, I remain in, I do simply is as a matter of my professional career, but I would also recommend to anyone else's keep yourself informed about clean energy policy, particularly in your area, right? Electricity demands are only going to increase. And again, the real issue with with AI, the real environmental impact with AI is its electricity draw. So if we were on a completely renewable grid, we could have a bunch of different conversations about the impacts of AI. But right now, that's that's the predominant and most pressing issue. So staying informed on that, supporting those, those types of policies and the people who uphold those policies is probably going to lead to better outcomes environmentally for all kinds of stuff, not just AI usage. So that's that's got a uh a sort of lead-on effect. But yeah, I would say that of all of those things, I would give that same advice to anyone about just about anything in the modern world, right? Where we're aware of the environmental impacts of of uh passenger vehicles. So the same sort of thing. Consider the necessity of driving a car. All of those things add up, right? And so again, while it's while it's not the individual's responsibility to solve those problems, we can engage with each other and engage with our environment in a in a I would say in a in a healthier way, as long as we sort of remain mindful and thoughtful about how we do it.

SPEAKER_00 30:46

That's great. I think the one thing I would add to that is that not all AI requests are equal in terms of energy consumption, right? So things like a basic text prompt is going to require much less energy than somebody, like you said, who's making a like AI generated video. And there's certainly, at least from my perspective, there's certainly a place for both, but it's that needless generation that I'm gonna make an AI video because I can make one versus I have a use case for one that makes the big difference. Um I think yeah, just recognizing our own tolerances is a huge message, right? Some people are gonna be more concerned about how to use an AI tool efficiently and effectively, and so they're gonna need practice to get to that point. Others are more concerned about the environment, so they're gonna focus less on the AI tool that they're using and more on, I guess, teaching themselves the best tips and tricks um before they start using it. So I think it's all it's all about that individual user right and our own preferences, but then also to your point, how that feeds back into that larger picture of collectively we can make a difference.

SPEAKER_01 31:59

For sure. And the thing that I'd I'd also like to say is that a lot of the sort of talking points about the promise of AI that have come to us from the the heads of some of these companies have been about AI's ability to solve these environmental problems for us. And I would say that maybe that's the best thing you can do, right? Is consider broadly what is the goal of what you're using AI to do. And so if it does, in fact, like give us the opportunity to radically reimagine our energy systems, right, to find ways to novel, novel battery storage that that reduces the demand on the grid or some sort of new breakthroughs to enable mass renewable energy deployment. If in fact, right, that's the case, right, and we can actually use this technology to help us solve some of our environmental challenges, then I think that that would be the best case scenario. And so my my real request would be if someone does have those environmental concerns, right, like let's work towards solving them because I think that that's really the only way that we're gonna get them figured out.

SPEAKER_00 33:13

Mike, this has been a great conversation. And as I said a few minutes back, it really helped me to feel a little bit at least a little bit more knowledgeable about what we're doing as a university and what I can do as an individual to be more mindful of my own AI use and habits. So thank you for that. If anybody has any questions for you, how can they best reach out to you?

SPEAKER_01 33:37

I can be reached directly at my email, m.weinstein at snhu.edu, but also anyone can always email sustainability at snhu.edu, and that'll that'll be handled by one of the amazing people on my team, either Pamela Beckvani or Jesse Carswell or myself. But that's always available. There's also the option to go to our sustainability SharePoint site and drop a line through that as well. There's a place to submit comments or questions. But yeah, I I want to reiterate that I'm always available for these conversations. I think these are some of the the important conversations we should be having around not just around AI, but around the university in general. And so I'm really happy to continue having them with people and to continue working towards solutions with everyone. And I'm really grateful for uh coming coming on here to speak with you and have this platform to to kind of talk with you and the broader SNOO audience about AI and sustainability.

SPEAKER_00 34:31

Yeah, and anytime you want to come back, you're always more than welcome. Thank you again for taking the time out of your day to chat about such an important topic. And thank you to everyone else listening to this. As always, stay tuned for more episodes and these questions and thoughts coming in. If you have any ideas for future episodes, much like this one, please always reach out to me directly, or you can email ai at sne two.edu. Thank you, everyone.