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

0:00On pioneers of AI, we often talk about the responsibility that comes with building powerful technology. AI isn't just one thing. It's a force reshaping medicine, education, science, and the stories we tell on screen. And for that future to work, it needs infrastructure that's built thoughtfully and deliberately. As the essential cloud for AI, core weave empowers pioneers at leading AI labs and enterprises with a purpose-built cloud made specifically to train and run the world's most advanced models. They help accelerate the breakthroughs that matter at scale. The big ideas, the moonshots, the futures we haven't imagined yet. Ready for anything? Ready for AI. To learn more about how core weave powers the world's best AI, go to coreweave.com slash ready for anything. I think AI can definitely be like a really useful tool to help address climate change and has all sorts of uses.

1:03Like 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. What is the biggest misconception that people come into these programs with? I think that there's a big set of people who think that AI is this thing that is going to be developed by a certain set of people, and then 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 AI and the AI ecosystem look like totally. I've heard some people and specifically actually read 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?

2:06No. My guest today is Priyadanti. She's assistant professor at MIT's Eeks and Lids. Eeks 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. Priyad 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 Elkall-Ubi and this is Pioneers of AI, a podcast taking you behind the scenes of the AI revolution. Priyad, welcome to Pioneers of AI. Thanks for having me on. 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.

3:07So I want to start at the beginning of your journey, you grew up in the United States. That's right, I grew up in Massachusetts. Oh, where? North and over. 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? Yeah, so I think kind of growing up in the US and then going to India, you both see kind of disparity in sort of, you know, resources and wealth kind of between countries. And then also in both cases, kind of 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 kind of people begging on the street kind of in the same block. And so I think I just grew up with this really kind of inherent kind of sense that like, you know, equity and inequity are just these huge challenges in our society. And I really wanted to address them. And so that is actually what kind of 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 kind of turned into a sustainability curriculum.

4:18And it was just emphasized there, like the extent to which climate is not just sort of an environment issue, but it's a people issue and one that kind of has disproportionate effects on those who are already affected by other kind of inequities and society. And so sitting there as a high schooler, I was like, wait, equity is already really bad. This thing, climate is going to make it even worse. Like, oh my gosh, I need to do something about that. And so that kind of inspired my route into working on climate change. That's amazing. I can really relate to, 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 a stark contrast inequality, not just into our countries, but into our countries. And we definitely like, I'm a kids experience that, you know, we go back every summer. 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?

5:19Yeah. So when we look at kind of the effects of climate change and kind of the extent to which we'll see kind of more droughts, more flooding, et cetera, et cetera. We tend to see that first of all, kind of if you think about kind of economies and livelihoods that depend on the weather being kind of what you want it to be, agriculture, right? And there is definitely a big relationship between, for example, like countries that are agricultural, dominant in terms of their GDP and kind of developing countries, right? So already right there, it's kind of the effect of like the weather changing, obviously impacts kind of agriculturally dominant countries. In addition, you have kind of people who live in island states and in coastal regions, they are going to be the ones who experience kind of 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 change in weather, the same climate hazard, they're kind of existing wealth and infrastructure impacts how well position they are to actually kind of deal with that change.

6:23And obviously like countries that are less resources or locations that are less resource, they generally have kind of less infrastructure to actually equip them to deal with that change. So kind of in all of these different ways, both sort of the climate hazards being unfortunately distributed as well as people's ability to adapt being kind of related to the wealth that they have historically had and the infrastructure that historically had, that's kind of where this disproportionate impacts aspect is driven from. Yeah. I want to zoom out and talk about AI's energy use and how it impacts the planet. We had Dr. Sasha Lachioni from Hugging Face to you know her. Yes, 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 and kind of she's on a mission to actually build climate benchmarks or energy use benchmarks. From where you sit, how bad is AI really in terms of impacts on energy use, water consumption.

7:26Yeah, what where do you think we're at? Yeah. And so kind of stepping back one thing I also want to mention is that I think when it comes to like AI, right? Like a lot of people in the general public and in the kind of public discourse have this notion of AI as like this one specific thing. So they're like, it's chat. It's like large generative models, etc. AI in reality as kind of I imagine the audience of the show knows right is sort of a diversity of technologies and a diversity of paradigms. And so I think that kind of then when we talk about this public discourse of like a question I very often get asked is right. Like is AI's energy use going to 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 not it's 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 kind of the push for like a specific type of large scale technology and like kind of a massive investment in sort of data center installation and like all of the

8:33energy 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 that's bolstering that is if we just develop this type of AI then it'll have benefits for health and climate and such when side of the types of innovation and deployment of AI that are necessary to make an impact on climate and health are not largely kind of scaling this particular paradigm of AI. Yeah. Yeah. So basically there are different paradigms of AI that can help us get there both both in terms of fixing the problem right and kind of addressing the underlying also causes of the issue. Absolutely. Yeah. Yeah. I kind of actually want to 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 small models, 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.

9:35Yeah. So one kind of approach that we use. I'll call it like engineering informed AI where we basically kind of think about in power grids, which is the area I work in. We have some knowledge of kind of the physics of how this power grid works. We have some existing knowledge of kind 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 kind of in our modern paradigm of power grids to 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 like time wearing renewables and distributed devices and all that kind of thing. 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 kind of physics guarantees that we need to make sure we're not breaking the power grid.

10:43So 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 like the literal physical equations or kind of robust control guarantees or various kind of things that we 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 like you both need to do this if you want your algorithm to satisfy a particular constraint not hallucinate right like make up stuff that is not exactly can't be applied to the real world. Exactly like for a power grid if you kind of wanted to do the equivalent of not hallucinate like some of these constraints that it needs to satisfy it is probability zero that like a machine learning model will kind of land on that particular constraint. So you need to make sure that happens and then also in doing that you kind of avoid like 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 like what the model actually needs to learn.

11:45Yeah, which also kind of underscores you do not need a generative like a 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. Yeah, so I mean I think there's this paradigm of like do you create like a task specific model that's like for one specific thing or do try to create a quote unquote general purpose model that does everything. And I think in reality like 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 like grid and stay much same underlying physical structure there are clearly some tasks that probably can benefit from sort of sharing kind of you know you share data between these tasks you share like the learning of structure between these tasks and that can enhance learning overall.

12:47But I think right now the approach is a little bit like we're going to start from this paradigm of general purpose and like 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 up and I think this sort of clusters we get are kind of closer to that like some smaller set of tasks then like this kind of everything all at once with with one model very cool. 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 yeah so I mean I think that kind of generally like with kind of clean powering of of data centers yeah you clearly need your grid to be clean. And so that means kind of yeah putting together like you know solar wind nuclear like it becomes really necessary to do this. But then of course that doesn't take care of kind of like the use of water and water stressed regions so there I'm less of an expert in this but like citing data centers in a place that's less water stressed but also like alternative cooling technologies etc.

14:01So I think there are all these kinds of approaches but I think fundamentally like when we look at kind of global decarbonization pathways these pathways rely not just on sort of like cleaning up electricity but also kind of being serious about energy efficiency and like not wasting. Energy and so I think it's both about kind of for sure right like for the data center growth that we as a society choose to accommodate we should make sure that's clean. But also like when I say choose to accommodate we should also think about this holistically in terms of like that renewable energy we're putting out there should that before 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 kind of planning the overall efficiency of the energy sector. In a minute how AI can be part of the solution to climate change and why Priya disagrees with LinkedIn co-founder and investor Reid Hoffman that's after a break.

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17:19With over 3 million downloads on hugging face LTX to gives you full model weights training frameworks and evaluation tools to build real production workflows. Don't settle for a first draft get the complete creative engine to reach your ideas full potential try LTX to today at LTX dot IO slash model. All right so let's talk about how AI can be part of the solution I've heard some people and specifically actually read Hoffman who's an investor and entrepreneur and a good friend of mine and the show. He describes AI as the platinum bullet to solve climate change. Do you agree? No, okay great. That's why 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 like better get better allowing us to like monitor crop yields to understand like agricultural adaptation like optimizing heating and cooling systems to be more efficient like accelerate science.

18:25I 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 like an important support role in many 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 sort of like still part of a broader system and a broader solution and kind of AI itself is not the thing solving everything. I mean, even to give a very simple example like many applications that are like forecasting or monitoring like I mentioned like forecasting solar power or monitoring crop yields. That's information, but that information needs to help someone make a decision. And so like 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 super super helpful, not a silver bullet. Yeah, okay, I love that. Okay, so I want to kind of tie that to your work then. So a few years ago you started climate change AI actually back in 2019.

19:26So 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 kind of pre and post, you know, November 2022. 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 where we definitely saw that there was a lot of potential for AI to kind of serve what is one of the most challenging problems we have to solve like to address climate change. And so we wanted to bring more awareness to kind of the topic of like 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 kind of going in with sort of a hammer looking for a nail. It means really kind of co scoping solutions like among technologists and kind of domain experts and kind of on the ground users. It means responsible and ethical AI considerations. So we were trying to both kind of raise awareness of like where is it that AI can play a role and also provide guidance about like how to actually go about doing this.

20:34And so we started by writing a paper called tackling climate change with machine learning, which was the brainchild of my co founder, David Rolnek, because he was actually trying to kind of coming from the AI side, get into the climate side. And was like, well, if I'm going to do the homework to figure out like what I should be up to like why not kind of share that homework with with everyone. And so he kind of 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 a climate. And I think it's very a it's a great survey of kind of what's happening in the space, but also in a way it's quite actionable, which is very cool. I appreciate that. Thanks. And yeah, and exactly. And so we put that out there. And we ran a workshop alongside it. And you know, there were, you know, hundreds of people at that very first workshop, like lines out the door, but also lots of questions about, okay, you've told me, you've intrigued me, you've told me this area is important. But like, what do I do? How do I get involved? How do I find collaborators? How do I fund funding? And so that's what kind of inspired the nonprofit.

21:39And so since then we've run, continue to run this workshop series so people can exchange knowledge. We've run kind of a summer school program to help kind of like democratize education on AI and climate and help more people kind of upskill and gain knowledge on this area. Or college or college or like the program tends to be geared towards people who are like, let's say like post college, but like kind of kind of spanning and late college as well. But kind of like kind of early professional like late grad student and kind of all the way up. We definitely have like senior people also kind of participating in these programs. And so yeah, and then also like grants programs. So basically trying to kind of provide ways for more people to participate and to kind of actualize solutions on the ground. And so then to your question of like, how is the mission kind of changed over time. I would say that at the beginning because honestly like many fewer people also in the general public even knew what AI was. Often in these settings, we were talking to like an audience that was like, you have your sort of initial niche audience that sort of is more in the know that already knows like some of the technologies and values around this.

22:46Or you are their first entry point to that. So like if we're talking to a climate person, it's like, we have the opportunity to be like, okay, this is what AI is. This is what responsible AI is like 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 chat GPT so they might come in with a specific or narrow topic. 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 time 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 kind of ensuring that people have the tools and skill sets and community and resources to like 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 kind of conceptions and misconceptions that people might come in with as well as ways that maybe people try to. So let's say co-opt this message that AI can be useful for climate and use it to maybe like push forward other agendas.

23:47What is the biggest misconception that people come into these programs with? Yeah, so I would say the kind of one of the ones I started with which is that like AI is one thing. So that when we're saying like AI for climate, it's like LLM's for climate. So that's definitely one major misconception. And yeah, I think this sort of I think many more people are coming in with this like AI is a silver bullet like aspect. And again, AI is super useful, but I think kind of helping to people to situate like when and where is it useful. And then I think another one that maybe is more like subtle but comes from this like AI is one thing. I think that there's a big set of people who think that AI is this thing that is going to be developed by a certain set of people and then like the rest of the world will like procure it and use it. And you kind of see like co-option of word 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.

24:48Totally. When democratization is used as terminology elsewhere, it's like, oh, more people can use these tools. And I think it's the because they're not shaping it, right? They're just consumers of it. They're just consumers of it. And that means that like 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 and the way you do that in India is different than the way you develop AI for energy systems in the US. And is that's different than AI for something else, right? And so because there's this conception of AI being one thing, I don't think people recognize the extent to which like there's a lot of fruitfulness and more people participating in kind of developing tools shaping the ecosystem and kind of the fact that it really needs to be this ground up effort. If we want AI to be developed in ways that are most societal useful. I love that. And that like includes diversity of backgrounds and thought and geographic diversity and exactly I love that.

25:48Yeah. One of the things I also love about what you're doing at Climate Change AI is you're building these data sets and you're making them publicly available to researchers and folks. Why is that important? And can you give us examples of what these data sets include? For sure. And let me clarify this a bit too. So kind of the two ways we've engaged in this sort of data space. One of them is sort of an assessment of data gaps. So trying to kind of call it information about like 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 kind of the state of that data? One of things that does the data even exists. So do you have to create that data set. But also there are various other things that can be kind of challenges like is the data clean? Is there enough storage space for it? Like that people can access. So if you're thinking about like bioacoustic data which is like right like you have like sound sensors essentially right microphones in various remote places.

26:48So you can kind of analyze that to understand the biodiversity. That's like a lot of continuously streaming audio data and the nonprofits who are collecting that data don't always have storage space to actually disseminate that. So interesting licensing right like who can use it. So trying to kind of pinpoint those kinds of aspects to then kind of enable people who are good at those different kinds of things to maybe hone in on like where can I contribute to kind of 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 kind of teams to work on particular like AI for climate challenges. And we kind of these fund projects across like energy and agriculture and buildings and shrimp aquaculture. And so in the process of that project, we also sort of ask the teams to produce a publicly available data set that others can then use to kind of do kind of additional work in these in these areas. And so yeah, so just to clarify, we don't like build data sets directly. Yeah, very cool and very important work because people who are in this space will know that like just getting access to the data is often the bottleneck for a lot of this research.

27:56And it's really expensive and time consuming. Yeah, and challenging and geographically disparate to come back to this thing right. 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 like almost like the same argument in healthcare. It's like we have insulin, but it's we haven't solved diabetes if not everyone can actually access that. So it's sort of something similar. There's like the technical aspect that there's like the what is it actually take on the ground for the problem to be solved. So I want to now kind of dig into specific examples and maybe we can kind of go through those quickly. Sure. So let's start with AI and the power grid. How can AI make power grids more efficient? Yeah, so AI can both help make power grids more efficient and sort of enable the integration of renewables. So that's I think the two kind of big things that motivate my work by kind of improving forecasts of solar wind and demand. By helping us to sort of speed up the algorithms that we used to optimize power grids to kind of contend with this era of like lots of renewables distributed devices.

29:04And just the fact that we have to optimize these systems at scale. And then also help us envision kind of new paradigms for both operating these power grids that are in a way that's more like distributed or localized or like sort of edge computed. As well as new paradigms for planning power grids by allowing us to do better like scenario generation or have better physical representations of the power grid within our planning models. How do you then deploy all of this? Yeah, so it depends on the specific application of course. So when it comes to something like forecasting or like monitoring or predictive maintenance or these kinds of things. These are situations where like the algorithm is providing information to a decision maker. And that means that you can also you can often run it inside car mode alongside the existing information provision algorithms. And then you can kind of stack it up and see like how well did that do. And then over time, if you're like, okay, it's like performing quite well, then 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?

30:08It gets trickier because there's no like exact replica 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 like some of our work also and work of others as well also looks at this idea of how do you create better like simulators test digital twins. Yeah, but like sometimes even dumber than digital twins because like all of these things basically are trying to stress tests like how does your model kind of evolve the system. But if your models are not super duper advanced yet. And you throw them at a very advanced digital replica like there's a mismatch. It's just going to flounder. And so there's some art tell so like constructing these simulators in a way that are hard enough that they represent something realistic about the system. But they also like push forward the innovation that they're tractable enough to push forward innovation some amount. Using AI to make power grids more efficient is huge. But as an investor, I still have lots of questions about the commercial viability of climate AI initiatives.

31:11After a break, Priya makes the case for funding these projects and gets honest about doing research under a hostile Trump administration. When it comes to AI, we spend a lot of time talking about what it takes to move models from research into reality. Enter LTX-2 from light tricks. It's an open source audio video foundation model built for synchronized sound and video, native 4K output, expressive motion and precise multimodal control all running on consumer GPUs. With over 3 million downloads on hugging face, LTX-2 gives you full model weights, training frameworks and evaluation tools to build real production workflows. Don't settle for a first draft. Get the complete creative engine to reach your ideas full potential. Try LTX-2 today at LTX.io slash model.

32:22So let's talk about the hurdles in progress in climate AI. And I want to start with funding. So government funding for climate initiative fluctuates dramatically, depending on the administration and 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 kind of research with your colleagues? What does the landscape look like? Yeah, it has. So for example, I was part of like a big department of energy funded consortium on kind of more sustainable and resilient energy systems planning. And that kind of got canceled, right? So that's one effect, but definitely there are lots of other people who are kind of, you know, their 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. It's like they were completely relying on kind of federal funding. So those like institutes organizations like may not exist anymore.

33:23So that's definitely a challenge. I think that luckily in some of these areas, like the kind of economic bottom line aligns with kind of the desired decarbonization strategy. And I think those are places where there still exist kind of like alternative mechanisms to finance the research and deployment. So with kind of renewables and wind, like they are cheaper to deploy, right? Then kind of fossil fuel related energy at this point. And so kind of in settings where we are allowed to like push those economically aligned things forward, then 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 kind of hurdle to navigate. But in many of these cases where like, yeah, a private industry basically has incentive to come in and fund it, that's good. Now, I think where colleagues are struggling or cases where like you sort of don't have that where you are trying to do stuff like AI for biodiversity and sort of ecosystem services are chronically undervalued.

34:27And often who's working in these spaces are NGOs, not necessarily well funded companies. So I think kind of it becomes increasingly important to come up with like clever markets and financial and business models in these cases to try to align private financing with making progress on those things. Yeah, I will say like I love this space, but as an investor, I invest in early stage AI startups. And it's sometimes a struggle in this space to kind of map out, okay, what is the commercialization route look like? How do you create scale? Like sometimes, yes, you can see how this particular algorithm or technology is going to solve a problem. But how is it going to solve it at scale if you have any thoughts around that? Yeah, you know, I have no silver bulletin here unfortunately, but yeah, that's what you're saying, though, is like 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 kind of the overlapping Venn diagrams?

35:27And I think there are two also thinking about like sometimes I feel like there's this mental dichotomy that some folks have. It's like, it's VC fundable or it's not a profitable business. But yeah, like there's an in between, right? Like some things are profitable businesses, but not hockey stick ones. And so I think also kind of helping people find that fit of like 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. Very true. Part of your work involves educating and advising policymakers, 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? Yeah, so I mean, I think of course it's important to contextualize, right? Like public policy is not just US public policy. So I think there's an extent to which also kind of identifying places where there is still that interest and leverage and kind of like helping those governments and such to kind of make progress on these is obviously super important.

36:29But even in the US context, I think there's still like, there's still so many places where kind of what needs to happen to make progress on climate change is directly aligned with kind of goals like affordability and resilience and other things that are important that people do care about. And so I think it ends up being a bit about trying to identify those points of convergence and sort of seeing where there's the capability to make progress on those. What about us as like individuals, right? Like I imagine there'll be a lot of people listening to this conversation and thinking, but what can I do, right? Like on a day to day basis? Yeah, so I mean, obviously there are kind of the individual, like the sort of most impactful things for individuals to do to reduce their own carbon footprint. So that's things like, you know, switching to public transportation or changing to more like plant based diets, for example, can actually have a relatively big impact when it's done at scale. But I also think that because so many of the important changes are systemic kind of using our voices as employees and citizens is also really important. And so that is things like corporate climate action doesn't have to wait for governmental climate action.

37:38You can for example, like a company can choose to set an internal carbon price or to like change their business strategy to be more climate aligned and pressure from employees and from customers can help drive that. And so like kind of using yeah, our role as like consumers and people in a workplace to try to kind of do that. As well as like governmental level, like I said, local government is so important. So like also engaging not just like with your vote and nationally and all of this, but also engaging in sort of local strategies to try to move the needle on climate is something like go to city or town hall meetings, like join grassroots organizations locally. I think there's a lot that can be done at the local scale. All right, last question. I want to end on a hopeful note. So what gives you hope that we can solve this? I think that whenever I kind of work with people in the AI and climate space, they are some of the most like intelligent, motivated, just like amazing people to work with. And so I think what really kind of gives me hope is the extent to which people care and the extent to which like there's so much ingenuity kind of going into addressing these challenges.

38:43And I also am hopeful because there's so much disagreement about how we go about this. Because as I mentioned, you actually need right like kind of 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. That gives me a lot of hope. Amazing. Yeah. Priya, thank you so much for joining us today. Yeah, thank you. I loved talking to Priya about how AI can help address climate change. But what stuck out to me the most is Priya's take on democratizing access. It's a buzzword in AI, but I particularly loved Priya's definition. It's not just about democratizing access to tools. It's about democratizing who makes these tools too. And it's why I'm personally so passionate about backing AI founders who tackle novel problems with AI and do so by bringing a diverse set of people and perspectives to the table.

39:46As always, thank you so much for listening. We'll be back in your feeds next week. Pioneers of AI is a weight-watt original production. Our executive producer is Eiff Tro. Our producer is Rachel Ishikawa. Our senior talent executive is Stephanie Stern. Mixing and mastering by Brian Pute. Video editing by Eric Purcell. Original music by Ryan Holiday. Our head of podcasts is Lita Malat. You can join the conversation across social media platforms. Just look for us at Pioneers of AI. Thanks so much for listening. Thanks for listening.