We have a power grid problem. Can AI fix it?

Note: Transcripts are automatically generated from episode audio, and are not fully corrected for spelling, grammar, and formatting.

PRIYA DONTI: I think AI can definitely be like a really useful tool to help address climate change and has all sorts of uses, right? Like better forecasting, renewable energy on the grid, like optimizing heating and cooling systems to be more efficient. But I would say that in all of these cases, AI is playing kind of a support role.

RANA EL KALIOUBY: What is the biggest misconception that people come into these programs with?

DONTI: I think that there’s a big set of people who think that AI is this thing that is gonna be developed by a certain set of people and then like the rest of the world will like procure it and use it. They’re not shaping it, they’re just consumers of it.

When I say democratization, what I mean is that more people are equipped with the skills, tools, and expertise to actually shape what AI and the AI ecosystem look like.

EL KALIOUBY: I’ve heard some people, and specifically actually Reid Hoffman, who’s an investor and entrepreneur and a good friend of mine. In the show he describes AI as the platinum bullet to solve climate change. Do you agree?

DONTI: Agree? No.

EL KALIOUBY: My guest today is Priya Donti. She’s assistant professor at MIT’s EECS and LIDS. EECS is electrical engineering and computer science and LIDS is MIT’s Lab for Information and Decision Systems. She’s also the co-founder and chair of Climate Change AI, a nonprofit tackling climate change using AI and machine learning. Priya was also named by Time Magazine as one of the 100 most influential people in AI for 2025. I’m so excited for my conversation with her. So let’s jump in.

I’m Rana el Kaliouby and this is Pioneers of AI.

[THEME MUSIC]

Priya, welcome to Pioneers of AI.

DONTI: Thanks for having me on.

EL KALIOUBY: I’m so excited to have you on the show and I’m especially excited that we’re doing this in person. That’s always the best.

DONTI: So I wanna start at the beginning of your journey. You grew up in the United States.

EL KALIOUBY: That’s right. I grew up in Massachusetts.

Oh, where?

DONTI: North Andover.

Copy LinkHow climate injustice shaped her path into AI

EL KALIOUBY: Oh, how cool. Yeah. That’s great. But as a kid you would go back and visit family in India. How did these experiences kind of shape the work you do today?

DONTI: Yeah, so I think growing up in the US and then going to India, you both see kind of disparity in resources and wealth between countries and then also in both cases within countries. And in India, sometimes that can feel particularly stark, where in a particular block you have like this really grand building and then people begging on the street in the same block. And so I think I just grew up with this really inherent sense that equity and inequity are just these huge challenges in our society. And I really wanted to address them. And so that is actually what inspired my route into working on climate change.

Because I was lucky to get to take a high school class where the first week of our biology class was turned into a sustainability curriculum and it was just emphasized there, the extent to which climate is not just an environment issue, but it’s a people issue.

And when that kind of has disproportionate effects on those who are already affected by other inequities in society. And so sitting there as a high schooler, I was like, wait, equity is already really bad, and then this thing, climate is gonna make it even worse. Like, oh my gosh, I need to do something about that. And so that inspired my route into working on climate.

EL KALIOUBY: That’s amazing. I can really relate to that. So I’m originally from Egypt and we spend a lot of time going back and forth between the US and Cairo. And like you I think there is this stark contrast of inequality, not just inter countries, but intra countries, and my kids experience that. We go back every summer and that’s always something we talk about.

Can you unpack for us this kind of disproportionate impact of climate effects on certain communities and what that inequity looks like?

DONTI: Yeah. So when we look at the effects of climate change and the extent to which we’ll see more droughts, more flooding, et cetera, we tend to see that first of all, if you think about economies and livelihoods that depend on the weather being what you want it to be, agriculture — and there is definitely a big relationship between, for example, countries that are agriculturally dominant in terms of their GDP and developing countries, right? So already right there, the effect of the weather changing obviously impacts agriculturally dominant countries.

In addition, you have people who live in island states and in coastal regions. They’re gonna be the ones who experience the biggest effects of sea level rise and flooding. And then even if you sort of have two different communities that in some sense experience the same climate hazard, their existing wealth and infrastructure impacts how well positioned they are to actually deal with that change. And obviously countries that are less resourced, or locations that are less resourced, they generally have less infrastructure to actually equip them to deal with that change.

So in all of these different ways, both the climate hazards being unfortunately distributed as well as people’s ability to adapt being related to the wealth they have historically had and the infrastructure they’ve historically had — that’s where this disproportionate impacts aspect is driven. Yeah.

Copy LinkWhy climate change hits vulnerable communities first

I wanna zoom out and talk about AI’s energy use and how it impacts the planet. We had Dr. Sasha Loni from Hugging Face. Do you know her? Yeah. We had her on the show. She was awesome. She leads all the climate efforts at Hugging Face and she talked about how hard it is to actually get a sense of the energy use of different models because these AI companies are not disclosing that information. She’s on a mission to actually build climate benchmarks or energy use benchmarks. From where you sit —

EL KALIOUBY: How bad is AI really in terms of impacts on energy use, water consumption? Where do you think we’re at?

DONTI: Yeah. And so, stepping back, one thing I also wanna mention is that I think when it comes to AI, a lot of people in the general public and in the public discourse have this notion of AI as like this one specific thing.

EL KALIOUBY: ChatGPT, ChatGPT.

DONTI: It’s like large generative models, et cetera. AI in reality, as I imagine the audience of this show knows, is a diversity of technologies and a diversity of paradigms. And so I think that when we talk about this public discourse, a question I very often get asked is, like, is AI’s energy use gonna be made up for by the fact that you can use AI in the electricity sector?

But in reality, the type of AI on both sides of that equation is usually not the same type of AI. And so basically I think that at a macro level, I am worried about the extent to which the push for a specific type of large-scale technology and a massive investment in data center installation and all of the energy and water and materials impacts of that —

I’m worried about that and I’m also sort of annoyed by the fact that sometimes the narrative bolstering that is: if we just develop this type of AI, then it’ll have benefits for health and climate and such, when the types of innovation and deployment of AI that are necessary to make an impact on climate and health are not largely about scaling this particular paradigm of AI.

EL KALIOUBY: Yeah. So basically there are different paradigms of AI that can help us get there, both in terms of fixing the problem and addressing the underlying causes of the issue.

DONTI: Absolutely. Yeah.

Copy LinkLooking beyond generative AI to climate focused models

EL KALIOUBY: I actually wanna dig into this more, because a lot of your work is rooted in machine learning, but it’s also not necessarily these large language models. My understanding is you do a lot of work with smaller models, but what else? Like what are the different approaches that you use in AI that perhaps don’t have the same climate impacts?

DONTI: Yeah, so one approach that we use, I’ll call it engineering-informed AI.

Where we basically think about — in power grids, which is the area I work in — we have some knowledge of the physics of how this power grid works. We have some existing knowledge of the engineering constraints associated with like, what you can tell equipment to do or not do, and —

The challenge we’re often trying to solve there is: if you just write down the physics and engineering constraints and try to sort of solve out like what should I do on my power grid, that tends to be, in our modern paradigm of power grids, too computationally expensive to do. You have to solve these problems of like, what do I do, much more quickly?

And at much more scale because you have more time-varying renewables and distributed devices and all of that. And so in that setting, what we try to do is develop AI techniques that can actually help us to optimize the grid faster and more effectively, but while still keeping some of the physics guarantees that we need to make sure we’re not breaking the power grid. Yeah. So there, the paradigm — when I say engineering-informed AI and machine learning — is we actually think pretty deeply about where are the opportunities to actually embed the literal physical equations or robust control guarantees or various things that we know how to grasp and prove into the way we design our neural network architectures in the first place.

And that way you kind of avoid also — let’s say you both need to do this if you want your algorithm to satisfy particular —

EL KALIOUBY: And not hallucinate, right? Like make up stuff that can’t be applied to the real world basically.

DONTI: Exactly. Like for a power grid, if you wanted to do the equivalent of not hallucinate some of these constraints that it needs to satisfy, it’s like, it is probability zero that a machine learning model will just land on that particular constraint. So you need to make sure that happens. And then also in doing that, you avoid wastefully relearning stuff from scratch from data because you already have that knowledge embedded in the model.

So you can sort of focus your training cycles and data size and data curation efforts on what the model actually needs to learn. Yeah.

EL KALIOUBY: Which also kind of underscores you do not need a generative large language model that can answer what can you have for dinner tonight to solve this problem. Right? Like, you can train it to focus on this specific problem. I think there’s an approach today where the solution to everything is this large generative general model, but that’s not necessarily the case.

DONTI: Yeah, so I think there’s this paradigm of, do you create a task-specific model that’s for one specific thing, or do you try to create a general-purpose model that does everything?

And I think in reality the true answer is somewhere in between, but closer to the task-specific side in the sense that even on the power grid, there are different tasks that — because they’re sharing the same physical grid and the same underlying physical structure — there are clearly some tasks that probably can benefit from sharing data between them and sharing the learning of structure between them, and that can enhance learning overall. But I think right now the approach is a little bit like, we’re gonna start from this paradigm of general purpose and see what it can apply to. And I think we actually need to go the other way.

It’s more like you start with your specific task. You try to understand if there are other tasks that could benefit from sharing knowledge, and you kind of build.

EL KALIOUBY: Build.

DONTI: And I think the clusters we get are closer to that, like some smaller set of tasks than this everything-all-at-once-with-one-model approach.

EL KALIOUBY: Very cool.

Copy LinkWhat sustainable AI infrastructure should actually prioritize

Okay. So there’s a lot of talk about the infrastructure needed to support the increased demand for AI, whether that’s building more data centers in the US or transitioning to nuclear power more widely. What kind of infrastructure do you think we will need to sustain this demand for AI?

DONTI: Yeah, so generally with clean powering of data centers, you clearly need your grid to be clean. So that means putting together solar, wind, nuclear — it becomes really necessary to do this. But then of course that doesn’t take care of the use of water in water-stressed regions.

So there, I’m less of an expert in this, but citing data centers in a place that’s less water stressed, but also alternative cooling technologies, et cetera. So I think there are all these kinds of approaches. But I think fundamentally, when we look at global decarbonization pathways, these pathways rely not just on cleaning up electricity, but also being serious about energy efficiency and not wasting energy.

And so I think it’s both about, for the data center growth that we as a society choose to accommodate, we should make sure that’s clean. But also, when I say choose to accommodate, we should also think about this holistically in terms of: that renewable energy we’re putting out there — should that be for data centers? Are there other types of loads and needs that we actually need to make sure we’re prioritizing and powering? And what does that mean for planning the overall efficiency of the energy sector?

EL KALIOUBY: In a minute, how AI can be part of the solution to climate change. And for my techno-optimists out there, Priya asks us to take off our rose-colored glasses for a moment. That’s after a break. All right, so let’s talk about how AI can be part of the solution. I’ve heard some people, and specifically actually Reid Hoffman, who’s an investor and entrepreneur and a good friend of mine. In the show he describes AI as the platinum bullet to solve climate change. Do you agree?

DONTI: Agree? No.

EL KALIOUBY: Okay, great. Tell us why.

DONTI: So I think AI can definitely be like a really useful tool to help address climate change and has all sorts of uses, right? Like better forecasting, renewable energy on the grid, allowing us to monitor crop yields to understand agricultural adaptation, optimizing heating and cooling systems to be more efficient, accelerating science.

I think there are lots of places AI can play a role. But I would say that in all of these cases, AI is playing kind of a support role — an important support role in some of those cases. And in some cases, like optimizing next-generation power grids, I can’t think how we would do it without AI, but it’s still part of a broader system and a broader solution, and AI itself is not the thing solving everything.

Yeah. I mean, even to give a very simple example, like many applications that are forecasting or monitoring — like forecasting solar power or monitoring crop yields — that’s information, but that information needs to help someone make a decision, right? And so who that decision maker is and what our decision-making processes are — if you don’t also think through that, then you don’t actually have a solution. This is just an algorithm lying somewhere. So yeah, AI is super helpful. Not a silver bullet.

Copy LinkWhy AI is a tool for climate action not the whole solution

EL KALIOUBY: Yeah. Okay. I love that. Okay, so I wanna kind of tie that to your work then. So a few years ago you started Climate Change AI, actually back in 2019. So that was before the whole generative AI moment. Why did you start this organization? And I also love your take on how has the mission changed pre and post November 2022?

DONTI: Yeah, totally. So I would say that when we started Climate Change AI, that was at a moment in time when people were not putting AI and climate change in the same sentence.

And yet we definitely saw that there was a lot of potential for AI to serve what is one of the most challenging problems we have to solve — addressing climate change. And so we wanted to bring more awareness to the topic of where is it that AI can play an impactful role, but also what does it mean to actually do this work in a way that’s impactful?

And that means, for example, not going in with a hammer looking for a nail. It means really co-scoping solutions among technologists and domain experts and on-the-ground users. It means responsible and ethical AI considerations. So we were trying to both raise awareness of where AI can play a role and also provide guidance about how to actually go about doing this.

And so we started by writing a paper called Tackling Climate Change with Machine Learning, which was the brainchild of my co-founder, David Rolnick. Because he was actually coming from the AI side, trying to get into the climate side, and was like, well, if I’m gonna do the homework to figure out what I should be up to, why not share that homework with everyone.

EL KALIOUBY: And so he got together a group of people, including myself, to write this Tackling Climate Change with Machine Learning paper. I love this paper, by the way, because there’s this table in it where you outline all the different ways AI can be applied to climate. And I think it’s a great survey of what’s happening in this space, but also in a way it’s quite actionable, which is very —

DONTI: I appreciate that. Thanks. And yes, exactly. So we put that out there and we ran a workshop alongside it. And there were hundreds of people at that very first workshop, like lines out the door. But also lots of questions about, okay, you’ve intrigued me, you’ve told me this area is important, but what do I do?

How do I get involved? How do I find collaborators? How do I find funding? And so that’s what inspired the nonprofit. And so since then we’ve continued to run this workshop series so people can exchange knowledge. We’ve run a summer school program to help democratize education on AI and climate and help more people upskill and —

EL KALIOUBY: For high school students or college students or like adult —

DONTI: So the program tends to be geared towards people who are post-college, but spanning late college as well — early professional, late grad student and all the way up. We definitely have senior people also participating in these programs.

And so yeah, and then also grants programs. So basically trying to provide ways for more people to participate and to actualize solutions on the ground. And so then to your question of how has the mission changed over time? I would say that at the beginning, because honestly many fewer people in the general public even knew what AI was —

Often in these settings we were talking to an audience where you have your initial niche audience that is more in the know, that already knows some of the technologies and values around this, or we are their first entry point to that. So if we’re talking to a climate person, it’s like we have the opportunity to say, okay, this is what AI is, this is what responsible AI is.

This is how to think about AI in the context of climate. Now, of course you have many more people who are gaining many more entry points into this topic. Their entry point might be ChatGPT, so they might come in with a specific or narrow view. And then also, whereas we started in a realm where AI and climate weren’t used in the same sentence —

Now it’s used in the same sentence all the —

EL KALIOUBY: The time. All the time. Right.

DONTI: For better and for worse. And so there’s an extent to which some aspects of our mission I think have stayed very similar. It’s still about ensuring that people have the tools and skill sets and community and resources to do AI for climate in an impactful and responsible way.

But because the broader context has changed, we have to also be much more responsive to conceptions and misconceptions that people might come in with, as well as ways that maybe people try to co-opt this message that AI can be useful for climate and use it to push forward other agendas.

Copy LinkThe myths that hold back meaningful AI for climate work

EL KALIOUBY: Yeah. What is the biggest misconception that people come into these programs with?

DONTI: Yeah, so I would say one of the ones I started with, which is that AI is one thing. So when we’re saying AI for climate, it’s like LLMs for climate. So that’s definitely one major misconception.

And I think many more people are coming in with this AI-is-a-silver-bullet aspect. And again, AI is super useful, but helping people to situate when and where is it useful is important. And then I think another one that’s more subtle but comes from this AI-is-one-thing idea —

I think that there’s a big set of people who think that AI is this thing that is gonna be developed by a certain set of people and then the rest of the world will procure it and use it. And you see co-option of words like democratization. When I say democratization, what I mean is more people who are empowered with the skills, tools, and expertise to actually shape what AI and the AI ecosystem look like. When democratization is used as terminology elsewhere, it’s like, oh, more people can use these tools — but they’re not shaping it, right? They’re just consumers of it.

And that means that the tools are not contending with the actual on-the-ground needs and realities of their problems, right? Like the way you develop AI to deal with energy systems in India is different than the way you develop AI for energy systems in the US. And that’s different than AI for something else. And so because there’s this conception of AI being one thing, I don’t think people recognize the extent to which there’s a lot of fruitfulness in more people participating in developing tools, shaping the ecosystem — and the fact that it really needs to be a ground-up effort if we want AI to be developed in ways that are most societally useful.

EL KALIOUBY: Love that. And that includes diversity of backgrounds and thought and geographic diversity. Exactly. Yeah. I love that. That’s pretty awesome. One of the things I also love about what you’re doing at Climate Change AI is you’re building these data sets and making them publicly available to researchers and folks. Why is that important? And can you give us examples of what these data sets include?

DONTI: For sure. And let me clarify this a bit too. So the two ways we’ve engaged in this data space — one of them is an assessment of data gaps. So trying to collate information about which data sets would actually be helpful in enabling or unlocking progress on a particular AI for climate application.

And what can we say about the state of that data — does the data even exist? So do you have to create that data set? But also there are various other things that can be challenges. Like is the data clean? Is there enough storage space for it that people can access?

So if you’re thinking about bio-acoustic data — you have sound sensors, essentially microphones, in various remote places. You can analyze that to understand biodiversity. That’s a lot of continuously streaming audio data. And the nonprofits who are collecting that data don’t always have storage space to actually disseminate it.

EL KALIOUBY: Interesting. Yeah. Licensing.

DONTI: Right? Like who can use it? So trying to pinpoint those kinds of aspects to then enable people who are good at those different kinds of things to maybe hone in on where can I contribute to improving the data landscape in useful ways. And then the other thing we’ve done is we run a grants program that’s focused on enabling teams to work on particular AI for climate challenges.

And we fund these projects across energy and agriculture and buildings and shrimp aquaculture and all of these —

EL KALIOUBY: That sounds fun.

DONTI: And so in the process of that project we also ask the teams to produce a publicly available data set that others can then use to do additional work in these areas. And so, just to clarify, we don’t build data sets directly.

EL KALIOUBY: Very cool and very important work because people who are in this space will know that just getting access to the data is often the bottleneck for a lot of this research, and it’s really expensive and time consuming. Yeah. And challenging and —

DONTI: Geographically disparate, to come back to this thing. Right? Like, so I think sometimes there’s this notion of like, we’ve solved AI for this particular application, but it’s like, have we solved it if we can’t actually actualize that application everywhere? It’s almost like the same argument in healthcare.

It’s like we have insulin, but we haven’t solved diabetes if not everyone can actually access it. So it’s something similar. There’s the technical aspect, but there’s also what does it actually take on the ground for the problem to be solved.

Copy LinkHow AI can help modernize and stabilize power grids

EL KALIOUBY: So I wanna now dig into specific examples and maybe we can go through those quickly. So let’s start with AI and the power grid. How can AI make power grids more efficient?

DONTI: Yeah. So AI can both help make power grids more efficient and enable the integration of renewables. Those are the two big things that motivate my work — by improving forecasts of solar, wind and demand, by helping us speed up the algorithms we use to optimize power grids to contend with this era of lots of renewables, distributed devices, and just the fact that we have to optimize these systems at scale. And then also help us envision new paradigms for both operating these power grids in a way that’s more distributed or localized or edge-computed, as well as new paradigms for planning power grids by allowing us to do better scenario generation or have better physical representations of the power grid within our planning models.

EL KALIOUBY: How do you then deploy all of this?

DONTI: Yeah. So it depends on the specific application, of course. So when it comes to something like forecasting or monitoring or predictive maintenance or these kinds of things, these are situations where the algorithm is providing information to a decision maker.

And that means you can often run it in sidecar mode alongside the existing information provision algorithms, and then you can stack it up and see how well did that do. And then over time, if it’s performing quite well, you integrate it in as your main information provision algorithm.

When it comes to some of these that are like, how do I actually optimize and control the power grid, it gets trickier because there’s no exact digital replica of the power grid that you can just try all these strategies out on and see how the system evolves over time. And so that’s where some of our work — and work of others as well — looks at this idea of how do you create better simulators, test beds, digital —

But sometimes even simpler than digital twins, because all of these things are basically trying to stress test how your model evolves the system. But if your models are not super advanced yet and you throw them at a very advanced digital replica, there’s a mismatch.

It’s just gonna flounder. And so there’s some art to constructing these simulators in a way that is hard enough that they represent something realistic about the system, but they also push forward the innovation enough to be tractable.

Copy LinkWhy funding and policy still slow climate AI progress

EL KALIOUBY: Enabling AI to make power grids more efficient is huge. But as an investor, I still have lots of questions about the profitability of these kinds of projects. After a break, Priya makes the case for funding AI climate solution projects and gets honest about what research has been like under a hostile Trump administration. So let’s talk about the hurdles in progress in climate AI. And I wanna start with funding. So government funding for climate initiatives fluctuates dramatically depending on the administration in power. And specifically under the Trump administration, we’ve seen a lot of cuts in R&D including clean energy projects. I am curious if that has affected your research. Has it affected research with your colleagues? What does the landscape look like?

DONTI: Yeah, it has. So for example, I was part of a big Department of Energy funded consortium on more sustainable and resilient energy systems planning.

And that got canceled, right? So that’s one effect. But definitely there are lots of other people whose grants get canceled in this area. And that means they either can’t work on individual projects, or in the case of some organizations or institutes, they were completely relying on federal funding.

So those institutes or organizations may not exist anymore. So that’s definitely a challenge. I think that luckily in some of these areas the economic bottom line aligns with the desired decarbonization strategy. And I think those are places where there still exist alternative mechanisms to finance the research and deployment.

So with renewables and wind, they are cheaper to deploy than fossil fuel related energy at this point. And so in settings where we are allowed to push those economically aligned things forward, that’s great. Obviously there’s retaliation at the federal level against wind and stuff like that.

Despite the fact that it is economically beneficial. So that’s another hurdle to navigate. But in many of these cases where private industry has incentive to come in and fund it, that’s good. Now I think where colleagues are struggling are cases where you sort of don’t have that, where you’re trying to do stuff like AI for biodiversity and ecosystem services are chronically undervalued, and often who’s working in these spaces are NGOs, not necessarily well-funded companies.

So I think it becomes increasingly important to come up with clever markets and financial and business models in these cases to try to align private financing with making progress on those things.

EL KALIOUBY: Yeah. I will say, I love this space, but as an investor I invest in early stage AI startups and sometimes it’s a struggle in this space to map out what does the commercialization route look like. How do you create scale? Like sometimes yes, you can see how this particular algorithm or technology is gonna solve a problem, but how is it gonna solve it at scale? If you have any thoughts around that.

DONTI: Yeah. I have no silver bullet answers here, unfortunately. But yeah.

EL KALIOUBY: Yeah, but that’s what you’re saying, is how do we align? Because there is a lot of interest in private funding to support these initiatives, but you also have to make the case for commercialization. So how do you find the Venn diagrams?

DONTI: I think there too, also thinking about, like, sometimes I feel like there’s this mental dichotomy that some folks have of, it’s VC fundable, or it’s not a profitable business.

Yeah, like there’s an in between, right? Like some things are profitable businesses, but not hockey stick ones. And so I think also helping people find that fit of what is the financial model that’s matched to the level of growth and profitability that you actually should expect from this solution is important.

EL KALIOUBY: Very true.

Part of your work involves educating and advising policy makers, but we are in this moment in time when climate change denialism is actually really strong. So what kind of friction do you experience when making these policy recommendations, and how do you go about that?

DONTI: Yeah, so I think of course it’s important to contextualize, right? Public policy is not just US public policy. So there’s an extent to which identifying places where there is still that interest in leverage and helping those governments to make progress on these is obviously super important.

But even in the US context, I think there’s still so many places where what needs to happen to make progress on climate change is directly aligned with goals like affordability and resilience and other things that are important and that people do care about. And so I think it ends up being about trying to identify those points of convergence and seeing where there’s the capability to make progress on those.

Copy LinkWhere individuals can still make a real difference

EL KALIOUBY: What about us as individuals, right? Like I imagine there’ll be a lot of people listening to this conversation and thinking, but what can I do? Like on a day-to-day basis?

DONTI: So obviously there are the individual, most impactful things for individuals to do to reduce their own carbon footprint. So that’s things like switching to public transportation or changing to more plant-based diets, for example, which can actually have a relatively big impact when done at scale.

But I also think that because so many of the important changes are systemic, using our voices as employees and citizens is also really important. And so that is things like corporate climate action. It doesn’t have to wait for governmental climate action. A company can choose to set an internal carbon price or to change their business strategy to be more climate aligned, and pressure from employees and from customers can help drive that.

And so using our role as consumers and people in a workplace to try to do that, as well as at the governmental level — local government is so important. So also engaging not just with your vote nationally and all of that, but also engaging in local strategies to try to move the needle on climate is something — go to city or town hall meetings, join grassroots organizations locally. I think there’s a lot that can be done at the local scale.

EL KALIOUBY: Very cool.

All right, last question. I wanna end on a hopeful note. So what gives you hope that we can solve this?

DONTI: I think that whenever I work with people in the AI and climate space, they are some of the most intelligent, motivated, amazing people to work with. And so I think what really gives me hope is the extent to which people care and the extent to which there’s so much ingenuity going into addressing these challenges.

And I also am hopeful because there’s so much disagreement about how we go about this because, as I mentioned, you actually need different approaches across policy, entrepreneurship, innovation. And so I think the fact that people are disagreeing means they’re talking about it and trying to think about what to do.

So I think that gives me a lot of hope.

EL KALIOUBY: Amazing. Well, Priya, thank you so much for joining us today.

DONTI: Thank you.

EL KALIOUBY: I loved getting to talk to Priya about these AI climate change solutions. And I’ll be thinking about what Priya said about democratization for a while. A lot of companies say they democratize access to tools.

But the tools themselves – the systems and platforms from the ground-up – can and should be built democratically. AI should constantly evolve to serve people and the planet, not just to make money.

A real democracy is messy – and needs real input from people. It’s one of the reasons why I am such a strong believer in bringing a diverse set of people and perspectives to the table at the launch of any new project.