Building Climate-Resilient Systems with AI

Very exciting sessions. I’ll just wait. Guys. So we are meeting for a tremendously important subject. And this has been a great summit. I know you’re all energized, inspired, excited and exhausted at the same time. And we will get a moment when this subject becomes a room of 5 ,000. So that’s what we’re going to work towards. But you’re here today. We’re delighted to have an absolutely tremendous panel with us today. I’m deeply honored, flown across from the U.S., from Europe, from Singapore and so forth. And we have a lot of material to cover. I should say that the triple challenge that we are dealing with in this panel is perhaps the most important challenge any of us will face in our lives.

Which is to promote development on the one side. While dealing with the creation of a sustainable planet. and in terms of climate change, your self -selecting group, you’re all here with us and there’s a reason for that. You already know about climate change. You already know about AI. Is that, as you know, we are not necessarily winning the battle on climate as yet and so we need to deal with both mitigation and adaptation and this panel will address both of those two things. We have very little time in the panel so I am going to speed along but that’s a good metaphor for the very little time we have to do something about climate change.

So we’re in action mode. It’s a call for collaboration. We’re not going to be, I apologize to our speakers and panelists for a number of things. One, this is not a real panel. We’re not going to be having discussions. This is just boom, boom, boom, talking to you about what everyone’s doing. Secondly, there’s going to be a kind of switcheroo moment when some other speakers come up and some of us are replaced up here. Apologies for that. It’s just the intensity of the panel and for all those things I apologize in advance but I don’t apologize. for the incredible quality of our panelists today. These are amazing people. And I would just end by saying that this is not a normal session.

This is an invitation for radical action -oriented collaboration with all of you. On that basis, let me begin by talking a little bit about a summit we held last year in London and the background to it and an organization that some of us are very deeply involved with and almost everyone here is a friend of called the Green Artificial Intelligence Learning Network. You will immediately note it has the cutesy acronym of GRAIL, like the Holy Grail. And what we’re trying to do is really see what the synergy is between the development agenda and the climate agenda through the application of AI. I’m going to speed through this. We’re going to then move to Professor David Sandelow, who actually anchored our summit last year, which is the first major global summit on the application of AI, to climate change.

and has very kindly flown in from Colombia. I’m going to ask our speakers one more favor, which is instead of my coming up and introducing all of you, if you don’t mind introducing yourselves, that will speed us along the way. So let’s go through this. So as you all know, and perhaps Professor Sandler will talk about it, the IPCC gave us a target, 43 % decarbonization from 2019 levels to 2030 levels. We were meant to reduce GHG emissions by that amount. In 2021, some of us at COP26 in Glasgow had a meeting to look at the likelihood of that happening. And we came to the conclusion that the likelihood was very low. And therefore, traditional approaches to climate mitigation and adaptation needed to be enhanced with new solutions.

And we thought, what was J -curving as fast as climate change was J -curving? And the only thing… There was nothing we could think of. There was nothing we could think of. There was nothing we could think of. was the application of AI, this great new suite of technologies, including, of course, quantum and all the other things that go with it. And we started to talk to people, and we talked to a whole bunch of people in the AI community, a whole bunch of people in the industrial and power, automotive, all the different sectors that produce emissions. And we said, are you talking to each other? And shockingly, people were not talking to each other.

There’s very little going on with some honorable exceptions at Google. Very few people were really in the AI community focusing on downstream issues around climate change. And similarly, the big industrial domains were not really focused on the use of AI for decarbonization and economic value creation. So with that lens, think about this session as throwing one J curve against another J curve. Can we throw the crazy increase in AI technology represented by this great summit against the world’s greatest challenge? That’s the purpose of the Grail organization, which is a not -for -profit based in London. It’s a vast terrain. We don’t have time to cover it all. It’s obviously mitigation. It’s obviously adaptation. We have to hit both.

And within that, there are endless taxonomies of all the wonderful things that AI can do. And, of course, you’ll be worried about the increased Gs from data centers, but that’s not the primary focus of our session today. That has been quantified. The Grantham Institute did a quantification last year of 0 .5 to 1 .4 gigatons of extra GHGs from data centers, but that’s from every kind of utilization against the potential benefit of 3 .5 to 5 .4 gigatons being sucked out of extra GHG emissions. So there’s clearly a very strong balance towards what AI can do to helping the planet in its shift towards a clean and green economy. Grail, what’s Grail? Grail is an attempt to create… …to create a collaborative network.

of great academic institutions, commercial institutions, AI companies, industrial companies, philanthropic institutions, private sector sustainability networks like WBCSD, bringing them all together with governments to try and create massive collaboration. In the next slide at the bottom, you see that same group of institutions. Bottom right, the ideas and deal flow. Going back into Grail, bottom left, the fact that this becomes a collaborative community to get all these solutions scaling at speed and at the top, then getting that deal flow funded through grants, through government programs, through venture capital, corporate funds, but to move the agenda to real solutions at massive scale as quickly as we possibly can. All of this led to a summit that occurred that I mentioned earlier last year.

Sean, will you keep me real on the time? Thank you. Okay. And that led to… 200 people. 115 organizations, including all the organizations represented here today, 60 speakers, and we looked at AI for power, AI for building materials, AI for everything you could think of vertically and horizontally, looking at the issues of materials innovation, looking at the issues of value chains, looking at carbon markets, and so forth. What has happened after the summit? Three things. One, we’ve created an online collaborative platform, and we invite all of you to join it, to co -create those solutions that can make a difference. Second, we’ve started to engage with governments around the world. Imagine a summit like this that was focused, yes, on development, but with a central climate focus as well.

How amazing would that be? And most importantly, we’re focusing on taxonomies that lead to massive calls for action from the innovation community. So we started to work on taxonomies for a variety of sectors, the energy sector, the built environment, materials innovation, and we worked with groups of AI experts and power experts and figured out what the big wins were, what are the big opportunities for companies to create economic value while at the same time massively decarbonizing. And this was an intellectual process, including many experts, some of whom are here today, that led to this astonishing work and identified the big win -win opportunities for economic value creation and decarbonization. On the bottom right, the teams from academia, industry, industry associations, a variety of other people and countries, eight country teams as well were involved in various ways.

So where’s this all going? Well, thank you to McKinsey for your very kind collaboration to after we had done all that work saying, hey, we want to help and kicking in and working with us to further refine those offerings and look at cost benefits and cost curves and all sorts of things. Delighted about it. And then there are, apart from working on the power sector, to look at generally what we can do, apart from working on the built environment, generally what we can do, apart from looking at materials innovation, generally what we can do to accelerate solutions for decarbonization through AI. We have two big partnerships that are emerging. One, and I want to slow down here.

Okay, 250 companies are part of the World Business Council, their network. That represents, in scope one, two, and three, 26 % of World GHGs. They’ve realized that that’s mainly in supply chains who are going into a partnership, so is McKinsey, so are other partners, to look at what are the AI opportunities to take startup and scale -ups into these decarbonization opportunities at massive scale with the 250 largest companies in the world representing 24 % of world revenues and 26 % of World GHGs. Finally, working with coalitions of energy companies, and Nalin, you’ll hear, and we’ll hear from later on, and we’re deeply partnered with Nalin. on this and others on this. How can we take this into accelerating?

For example, UNESA has 71 energy companies, 750 gigawatts of clean power. They want to go to 1 ,500 gigawatts of clean power by the end of the decade. How can we help them with AI? It’s a very practical lens. We invite you to join us and be part of this. On that note, I would like to briefly to invite to introduce Professor David Sandlow. David, I’m not going to go through your very distinguished background to take the whole panel to do that, except to say that you have worked in every different field, most importantly in the past in very senior positions in government, but now, of course, you’ve been the luminary on AI solutions on climate.

That is the worst introduction you’ve ever got. I’m sorry about that, just in the interest of time, but I’m… Really very honored that you’ve flown all the way to come here, and I’m handing them over to you. to you.

Thank you so much. Uday. Uday, thank you. Your energy, your enthusiasm, your passion, they’re all infectious. And your intellect is remarkable. What you’re driving forward in this area is world changing. You are not just an inspiration, you’re a gravitational force that are pulling people together to work on this, so thank you so much. Your, what you did in London was remarkable. What you’re doing here is incredible and I’m looking forward to being part of what you’re doing in the future. So I’ve, it’s been my privilege to lead some teams that have been working on these issues over the course of the past couple of years and I’m going to talk about one of the projects that we’ve done.

It looks like the slides are not there. There’s a certain, turning on the screen. There it goes. I will say that while we wait, I’ll say that I really like the metaphor that you had, Uday, about two hockey sticks. And this is just a remarkable convergence of two of the most important trends that are happening in human history right now. One of them is, alas, the increase in greenhouse gas emissions, which is happening at such an astonishing pace. But the second is the exponential growth of the capacities of artificial intelligence. What’s driving me is we need to find a way to make sure that that second trend, artificial intelligence, helps to solve the first problem.

And that’s the study that we did, which we brought together a team of 25 experts. Just wonderful people. One of them was . Song Lee, the head of the IP, last head of the Intergovernmental Panel on Climate Change, and some top experts. And the question we asked was, it was very simple, how do you use artificial intelligence to reduce greenhouse gas emissions? There it is. It came up on the screen. Thank you so much. I appreciate it. And so it’s a very simple question. How do you use AI to reduce emissions of greenhouse gases? We came up with 17 chapters. We wanted to do more than just provide analysis. We wanted to provide actionable ideas for what to do.

So every chapter has recommendations. You can find a print version available on Amazon, and free downloads of the entire volume, including chapter -by -chapter versions, are available at these websites. I want to thank the government of Japan, including NITO and MEDI, for supporting this work. They’ve been very important supporters of work on AI and clean energy more broadly. Oh, I’m going to promote my podcast later. But I have a podcast that’s talking about this topic as well. But so here’s our – Here’s our – Here’s the table of contents for our work. We talked – We have introductions to both AI and climate change in this volume. One of the things we’re trying to do is target this both to experts and to people who are beginners in this topic.

And, you know, Uday talked about bringing together different communities. One of our basic conclusions was we need to bring together experts in climate change and experts in AI. And there are a lot of people who know a lot about climate change but don’t know a lot about AI. A lot of people who know a lot about AI, they don’t know about climate change. So we decided to have primers on each of those topics. And then we talk about eight different sectors and a number of cross -cutting topics. So we have five key takeaways. This was an interesting exercise with all of our authors taking 300 pages and trying to distill it into five key takeaways.

But here’s what we came up with. The first one, I mean, this is a kind of bottom line, but it’s important. AI does have significant potential to contribute to reductions in greenhouse gas emissions. And we categorized it with two categories. One is climate change. It’s incremental gains such as just improving efficiency. It’s output itself. And then we have the other category, which is the environmental impact. solar farms, building energy efficiency. There are lots of incremental gains that can be made, but also transformational gains. In particular, new tech, new materials and other things. We looked at whether greenhouse gas emissions are causing increases in, or greenhouse gas emissions are increasing as a result of computing operations.

We decided, based on the available evidence, that the best estimate is less than 1 percent and maybe much less than 1 percent of greenhouse gas emissions are currently coming from AI. That tracks with the Grantham study that Uday talked about. That tracks with what the IEA has said as well. The main barriers to AI’s impact in reducing greenhouse gas emissions are a lack of data and a lack of trained personnel. There’s other barriers as well, but obviously you need data. A lot of places we don’t have the data for this purpose and you need people. Trust is essential. People aren’t going to use AI unless they trust it. And then every organization with a role in climate change mitigation should consider opportunities for AI.

And we need AI to contribute to its work. I think as AI grows in the public… consciousness at summits like this that’s becoming less and less of a kind of radical recommendation. But it’s just so important. I think if you’re working in climate mitigation, you need to have a team dedicated to AI and how AI can help. So I’ll just run through quickly because we only have a little bit of time, some of our chapters. We have a chapter on the introduction to AI, and if you’re a climate person that doesn’t know a lot about AI, this might be helpful to you. And one of the things we did is we broke down AI capabilities into four basic categories at a very high level.

The first thing AI can do is detect patterns. And how can that be helpful in climate change mitigation? Well, one example is detecting methane emissions from satellite data. You know, some of you probably know this, but I mean, we know much, much more today than we did 10 years ago about methane emissions. And that has helped us dramatically to begin to reduce methane emissions. That’s entirely dependent upon the optical sensing process. And we’ve been able to do that over the last 10 years. So, you know, we’ve been able to do that over the last 10 years. And we’ve been able to do that over the last 10 years. So, you know, we’ve been able to do that over the last 10 years.

And we’ve been able to do that over the last 10 years. And we’ve been able to do that over the last 10 years. So, you know, we’ve been able to do that over the last 10 years. And we’ve been able to do that over the last 10 years. impact so far. AI can also predict, such as weather patterns at solar and wind farms. It can optimize, such as power flows on transmission lines. And it can simulate, such as battery chemistry action. So I think for me, in fact, I’m teaching a course at Columbia right now where we’re emphasizing this framework of detecting, predicting, optimizing, and simulating. And those are, broadly speaking, the capabilities that AI brings to the table.

A lot to say about climate change, but just for those who aren’t paying attention, atmospheric concentrations of heat -trapping gases are now higher than any time in human history. In fact, higher than any time in the past three million years. And July 22nd, 2024, was the warmest day ever recorded. 2024 was the warmest year ever recorded by far. And the warmest 11 years ever recorded were in the last 11 years. So we are living in an era of climate change. We do deep dives into a number of different sectors. I’m just going to talk about a few of them. Power sector is… is maybe the most important just because it’s already 28 % of greenhouse gas emissions, and our strategy for reaching decarbonization requires us to electrify lots of things.

So we need to grow the power sector and decarbonize the sector at the same time. I don’t think we’re going to be able to do that without AI tools. AI is already helping decarbonize the power sector, optimizing location of generation transmission, increasing output at solar farms, but it can do much more. Dynamic line rating is optimal power flow analyses. But to do this, we need standardized data. We need trained personnel. The utility business model is a challenge. So this is a really important area that requires a lot of attention and work. Oh, and a final point, the last bullet on this slide. Using AI in real -time operations can cause real security and safety risks.

So we need to be very careful about generative AI in context. So even as we look to deploy AI to help reduce greenhouse gas emissions, we need to be very attentive to these risks. risks. I kind of find it amazing how few people pay attention sometimes to food systems and climate change, that 30 percent or more of greenhouse gases are in some way related to the food system, and the food system has, is threatened by climate change. AI can do a lot to improve both mitigation and resilience in the food system. We just, there’s a few examples here, integrating data from soil sensors to create fertilizer management plans, creating virtual farms. There’s lots of things that can be done here.

But coming back to this issue of lack of data, it’s a huge problem, especially in the Global South. So the efforts to build up a digital public infrastructure that are happening here in India are so important in this regard. I’m going to go quickly here. We look at buildings where there’s tremendous potential. I think materials innovation is one of the most important areas. And, you know, 150 years ago, when Thomas Edison invented the modern light bulb, he literally spent a year, he spent a year, he spent a year, he spent a year, he spent a year, he spent a year, he spent a year, he spent a year, running electricity through dozens, I think hundreds of different filaments to figure out how much light and heat would be produced.

So today, we can simulate a million of those interactions in a second. And there’s already tremendous advances in the pace of innovation in battery chemistry and some other areas using AI tools. And for me, this is one of the most promising areas in terms of transformational gains in reducing greenhouse gas emissions. Extreme weather response is extremely important from a resilient standpoint, and we don’t have a lot of time to get into it, but I think that the AI ML -enabled forecasting is transformational because it’s so much cheaper, for example. I mean, at really 1 ,000x the cost, 1 ,000x less the cost, we can run AI ML weather prediction tools and make a big difference on extreme weather response.

We have findings and recommendations throughout this report. You can see it here, again. We just did a new report in the same series on sustainable data centers. And our main message is there are that with this data center construction boom happening now, this is the time to be paying attention to data and sustainability. We are investing right now in multi -decade assets. We need to be paying attention to this. Smart siting is a key. And finally, here’s a plug for my podcast. It started about a year ago. I’ve had some great guests, Jensen Huang, Dami Lola, Ogunbi, the head of sustainable energy, for all Jennifer Granholm, the U.S. Energy Secretary under Biden. Listen, as they say, available on all major podcast platforms.

Uday, once again, thank

I feel horrified we’ve got speakers of this caliber and so little time. So thank you so much for your leadership. May I invite both of you to speak? You can speak from here if you prefer. We’ve got two great leaders from Google. Obviously, you know, in the sphere. of corporate AI leadership on climate, there is no one that parallels all of you and we look forward to hearing from you your thoughts. Thank you.

Thank you very much for hosting this. I don’t know if that’s a privilege or that’s pressure when you start with that sentence about the leadership position Google has. I just want a quick question. Raise of hands, how many of you have used Google today for either maps or searching something? Thank you. So you know who we are. This is my car. I’m Vrushali Gaud. I’ll introduce myself and then Spencer you can answer that. I lead Google’s in a nutshell decarbonization water and circularity strategy for the company. Essentially what that means is I’m responsible for quite a few things that you had in your slide that we should be doing and a good way to introduce myself is also I like getting things done and so I feel like my inner calling around this is we’ve had a lot of conversations we’ve had a lot of playbooks and research and things But it’s almost like, how do you act on it?

How do you execute on it? How do you start delivering the outcome that I think we all are looking for? So that’s the kind of space that I come from. And a privilege to be at Google, who allows us to kind of expand that space. So the reason I asked you all to show your hands, most of you know Google as a search or map and similar pieces, information source. One of the other things Google is now, I think, as a company, is a full -stack company. And when I say full -stack, that means the search and the information is a top layer of it. But underneath that sits the entire physical infrastructure that drives that.

And so that’s data centers. That’s the way you operate that. That’s the networks that feed into all of the applications. And so when we look at climate, and my title actually is Global Director of Climate Operations. So I say that out of humility because when we look at climate, we’re trying to put it across our operations the best we can. Thank you. So we, and good examples of that, I’ll start with data centers. The big topic right now, how do we operationalize them? Where do we cite them? The location, what impact it has on the community, what impact it has on the infrastructure there. Citing is a big part of it. Access to clean energy is something we’re looking at, and pretty much we have a carbon -free energy goal.

So I think for us, if you look at climate, a big portion of climate is emissions. How do we impact emissions? It’s from electricity. What do we do with electricity? Shift to clean energy. Or renewables. And so that’s the spectrum that we look at. And so a lot of our investments are in carbon -free energy and how we think about it. And it also is more not just take from the grid or expect the government or, you know, sort of the infrastructure to get you there, but how do we invest and bring more clean energy to the grid? I think that’s a big piece of, I think, what companies can do at the speed at which we are all moving is, how do we take these sort of bigger picture systems problems and embrace them and solve them?

So one is big, one is small. is generation of clean electricity, and the other is grid, and how do you solve the grid problems? So that’s the infrastructure of AI. Then using AI, I think that’s going to some of the other things, Professor, you were saying is, we look at how we could use AI to drive our operations more efficiently. It’s very boring pieces. It’s not really shiny superstar things, but a lot of the impact comes just in general. I look at water taps, and I remember the amount of leakages we have on water taps, the amount of electricity wires that are not connected, just the inefficient use of resources is a big one, and how can we use AI to sort of optimize, whether it’s optimizing within the use of our chips, optimizing the grid, optimizing which applications run from where.

That’s a big part of our strategy. And then the third piece is, what do you use AI, and how do you use it for climate? Now, clearly, our business is information and search, but which means we also have access to a lot of data. And so one of the ways we consider, as what you can do in AI is, how do you use these large data sets? A, find a way to open source them, encourage different use of them, but also incubate certain initiatives that can help to show the light to others. So Earth AI is a big one in which we you’ve got satellite images, you’ve got weather data, you’ve got all of these big chunks of information that we can put out there.

And then there’s an application layer, which I think is of interest to you in terms of resiliency or mitigation. So one of the things, you know, which you probably haven’t heard of as much is Flood Hub. So we have a lot of information put out there as to flood risks of different region, which then other companies can use for whatever products they’re launching, whether it’s insurance, whether it’s real estate, fire sat, wildfire risks. How do you do prediction around it? Something all utilities companies, especially from in California, where I’m based, is we’re very passionate about using that for prediction. I can go on about the list of sort of what data can be used and how it can be leveraged.

The thing I think I’m going to go back to the, you know, crux of what you had in this is. is we’re in the timeframe of two hockey sticks. One is the impact on emissions, and I completely appreciate that the tech companies, hyperscalers, data centers are at a scale contributing to it, which we want to obviously help mitigate or replace with clean electricity as much as we can. And then the other is, how do you use the innovation curve on this? And I think we’ve just scratched the surface. And there’s, of course, like, you know, there’ll be trials and errors, but the surface around how do we democratize data, how do we encourage innovation, and how do we scale it very quickly?

Because I think those are the three, the trifecta of how do you drive this change? And so one of the ways I’ll end with saying I’m super proud of what we’ve done this week, trying to bridge those two gaps is we’re working with the Principal Scientific Advisory of the Government of India to launch a Google Center of Climate Tech. We call it Climate Tech because it does, those are the two hockey sticks that you’re trying to get in, right? The tech scale and the climate impact. and we are our goal is to encourage academic research but research that is actionable so five pilots first of all kinds and how you can scale um and there’s a lot of uh you know focus already on electricity so we are trying to do the non -electricity pieces in that which is around low carbon steel low carbon materials built environments um low carbon sustainable aviation fuel and then the biggest one we don’t talk we talk about what i think is a big lever across everything is green skills you need to embed this sort of a thinking which is green climate first across every domain and how can we encourage that in in india and especially the tier two cities so super excited about those two hockey sticks and how we’ve as a company can bridge those gaps

Intensity of what is produced in this part of the world actually is really important globally. But what’s really distinctive about APAC is actually the third major topic, which is livelihoods. As I mentioned, this is the part of the world which has a lot of developmental ambitions, and livelihoods are key. So my colleague touched on, Vushali touched on Chapter 3, power systems. Actually, I would like to touch on agriculture and food systems, which is your Chapter 4. So agriculture and other land use is actually the largest employment sector in the Asia -Pacific. I mean, I believe in India it’s about 46 % of jobs. And for the region, it’s the largest sector, about the same as the next two sectors added together, which are actually manufacturing and wholesale and retail trade.

Add those together, you get the same number of jobs as in agriculture. Now, over 80%. 80 % of farms around the world, and especially in India and the rest of the global south, are smallholder farms. farmers. And that creates an issue because a lot of the technology for agriculture is developed for large commercial farms, satellite imagery, et cetera. So this is one example I’d just like to delve into in terms of what Google is doing to contribute to the data, the digital public goods that Rushali spoke of. So if you want to use satellite imagery and actually understand agriculture so you can do things with it, you need to find the boundaries of your individual farms.

That’s your individual unit and often less than two hectares, if not smaller. And so you can do that with people poring over maps or satellite imagery, but that’s not scalable. But this is a really interesting problem for AI. And so for those of you who’d like to know more about this, there’s actually an exhibit at the Expo at the Google Pavilion. But this is what we call agricultural landscape understanding and agricultural monitoring and event detection. So we’ve trained AI to actually digitally enhance the environment. The field boundary. and you can say, well, that’s interesting because you can zoom into India and look at the Indo -Gangetic plain and see all the field boundaries. We’ve also trained the model to distinguish what crops are being grown through multispectral imagery.

And with that, we can detect events like tillage, sowing, harvest, et cetera. And all this data is now available. It is part of the Krishi DSS. So it’s contributing to the digital public infrastructure of the Indian government through the Ministry of Agriculture, through state governments, for example, like that of Telangana and the ADEX system. And what this does is it allows NGOs, government bodies, et cetera, to actually give advice to farmers because you now understand what’s going on on the ground, which is a critical driver for mitigation benefits, but also adaptation as actually the planting and the growing of crops. So we’re seeing best practices for planting. and what to plant is actually changing over time with climate change.

So do find out more at the pavilion. But one thing that I like to double -click on, as we say, is actually the innovation part of it. This digital public infrastructure is only helpful if it can be really used. And it’s not just governments and NGOs. It’s also startups. They’re innovating and finding new ways of using this information. So companies like Carbon Farm, they’re in France. They’re using this data. Varaha, which is a social startup, entrepreneurship. But also, Wadwani AI is another startup that we are supporting as well in terms of driving innovation in the agricultural space. So this is really all going to be accelerated through the use of AI, and we’re very excited to contribute to that.

Wonderful. You can see – I’m going to just grab one of these. Thank you. So, hello. Yeah, you can see that Google represents the convergence of the two themes we were talking about. And I think you have a wonderful web. At least I’ve seen access to materials about your sustainability strategy online. So if people want to know more, I’m sure they can go there.

Yeah, I’ll make a plug. Website, sustainabilitygoogle .com. It has all of our information. And the expo booth has all of our information. So thank you very, very much.

Now, we are inevitably vastly behind schedule as we are with climate change. However, we’re going to keep going with great focus. And we’re going to turn to the energy and power sector. Now, this is a bit embarrassing because we have to do a little bit of a switchover of people. But we don’t have time to put up the new names here. So we’re just going to announce them and listen with great attention. So we have two fantastic speakers from the energy sector, which, as you know, is one, if not the most important sector. Do you want to come closer, Nalim? And we can just be together here. I would ask, obviously the decarbonization of the energy sector is absolutely critical without that nothing happens so I’m going to hand over straight away first to you if I may Nalin, to set the stage a little bit and what you’re trying to do with Climate Collective and UNESA and then Dan more specifically to what you’re up to so over to both of you and obviously introduce yourself sorry I haven’t done it for you

no, thank you I understand we are short on time so I’ll keep very brief I’m Nalin Agarwal, one of the founding partners of the Climate Collective I think we’ll have the slides up soon great, so today I’m just going to talk about very quickly a program that we’ve been running for 6 years and where we partnered with Graylon to really drive decarbonization and grid modernization starting with India but across the global south so if I can move on who’s operating that? let me go there to you I’ll do it. Okay, just quick snapshots. We are an ESO enterprise support organization, largest in the global south, about 1 ,500 startups supported. Key partnerships, UNESA is a key one.

I don’t want to spend too much time here, but that’s what we’re going to spend some time on as well. We do a lot of work in AI, in power but beyond. So next week we’re doing the Delhi Climate Innovation Week. In fact, Google is a sponsor and partner there, and of course, Grail is as well. But happy to chat about this later. Here’s what we’re trying to do. I think what’s happening is that a lot of the challenges on renewables are being solved. They will be solved. I think one of the increasing recognitions is that the grid is a key bottleneck now, and we need to really work on grid transformation. That includes both decarbonization and modernization.

So that’s what we’re working towards. So we work with utilities. There’s about 22 of those that we’ve worked with so far. Work on a problem statement approach. Get startups to apply. Select startups. Get them to create business cases and pilot plans and eventually lead to pilots, right? So there’s 22 utilities that have participated and have led to about 20 pilots, a subset of which have become large deployments, right? So it’s a very unique program in the global south, actually. It’s the only one, right? High conversion ratio, so about 30 % of the pilots that have been proposed have come in. Key partners, I mean, 22 utilities and all the people that are working in power sector reform are part of this program.

I mean, again, I won’t spend too much time, but there’s a lot of this information available online. All the startups that are vetted, ready to deploy, are available for utilities to engage with. we have a bunch of case studies also but the key point is this we are now developing this along with Grail into a global AI for power innovation platform which has three components the open innovation program which is electron wipe on the top there is the knowledge hub which is basically a peer sharing platform where we do convenings co -located at COPS at climate weeks etc and then there is an online solution database of pre -wetted solutions. I’ll stop there and hand it over to Dan

Thanks. Thanks Nalan I’m going to stand up too because I like to stand up and talk so my name is Dan Travers I’m from Open Climate Fix we are a startup doing AI for grid. I’m going to dive a little bit into the grid area which has been talked about a bit in order to get to net zero we need to electrify we need to green the grid and we need to electrify everything the grid of the past had Usually in each country there was tens of generators and the grid operator would know those people on a first -name basis and they would ring them up and tell them when to turn up and down.

We’ve now got millions of generators with solar panels, wind turbines everywhere. The grid of the past had variability from just demand. We’ve now got variability from demand and the wind speed and the clouds, right, so three sources of variability. The grid of the past had a normal demand that we understood well. We’ve now got data centres, we’ve got EVs, we’ve got batteries, we’ve got AC, so the demand is changing shape incredibly. How are you possibly going to address this balancing of this grid with a bunch of people in a room, right? You need AI solutions. You need a highly digital grid. You need something which can schedule and marshal all of these assets in a digital at sort of AI speed.

So… That’s really important. And why is it important… It’s important because if we don’t do it, we’ll have blackouts, and if we don’t do it, we’ll have costs increasing because the way that grid operators are currently dealing with this challenge is they’re actually scheduling a lot of backup generation. It’s usually gas -fired generation. It’s very expensive, so bills are going up. And if you look around what’s happening now, there’s a push back against the green revolution. If we don’t address these problems, we’re going to have a democratic pushback, and we will have a reversal. So AI solutions can really help us in fighting the battle for hearts and minds as well as the actual physical battle.

So myself, I came from sort of banking tech space. Jack, my co -founder, came from Google DeepMind, who the name keeps coming up. We both saw there was a big gap between the amazing tech that was available in some of these industries and grid operators and the electricity industry, which is by nature very risk -averse. It has to be worried about things failing all the time. So we saw the gap between those two, and we formed Open Climate Fix to really try and address that gap. to bridge that, to take sort of moonshot ideas and actually build a rocket ship that is going to fly to the moon and actually implement something and give data to researchers.

The company’s non -profit, we’re open source, and that’s about the scaling, which I think is a key part of the title of this talk. So we’ve built the best solar forecast in the UK, we think, by about 20 % or 30%, like quite a long way. We now want to take that, we are starting to take that to India. We’re working with Adani, we’re working with Rajasthan Grid Operator, and with a combination of open source plus commercial sort of expansion, we see the AI tools as super transferable across grids. So I’m really excited that we can take tools from one grid and apply them to all the grids in the world and use AI to solve climate change.

Thank you.

Thank you so much. You can imagine if we had more time, we would have had a panel on the built environment, a panel on industrial decarbon, a panel on transportation. We don’t have the time. But thank you for that fantastic presentation. A couple of interventions. Now we turn to the last segment. We have three very distinguished institutions with us, all involved at the strategic level. with Grail and with this process. And I’ll start, Ankur, with you at McKinsey, who have been close partners.

Thanks a lot. Another race against time. So what would I like to… I should say that Sean went out of the room and negotiated five minutes more for us in the room. So… Okay. So, while the slides come up, it’s firstly a privilege to be here. And thank you for the opportunity for McKinsey to be part of the journey that you are leading, Uday. And thank you all for being here and shaping this in your own special way, at your own scale. I’m Ankur. I’m a partner based out of our India office. I lead Quantum Black in India, which is our AI team. And I work across sectors, because that’s really… a lot of fun.

And part of my work has been in energy. Part of my work has been in the built environment. Thank you. but I’m representing the team which is quite global that has had the privilege to work with the GRAIL effort. So I’d like to just talk about how this little effort with us is shaping the larger movement that GRAIL represents. So everybody’s talking about the impact of AI, so I’m not going to talk more about that, but the promise of AI, let’s just be clear. I think the way large global efforts have sort of found shape is to focus around a few challenges. So one of the big pieces that the GRAIL work has been about is shaping these four challenges and articulating them.

They’re about operational improvement in our current way of working. Big consulting words, strategic intelligence and foresight, basically better planning. Okay, build things better. Transformation, innovation. So can we do new things that don’t exist? That will help the future. And the last one is autonomous operations, which is essentially do you do current operations in a very different way? Use drones instead of people to go see how the wiring is in a large electric plant. and create more impact. Several of the examples you heard about will fit into this across energy -built environment materials and this can keep expanding food systems perhaps. Now, there’s a huge amount of work going on in just collecting the knowledge on each of those challenges.

Then you think about those fields of play, the energy -built environment. Within that, there are stakeholders. So for each stakeholder, what’s relevant? And then for each stakeholder, let’s say if you talk about system operators here as an example, there’s network planning is a domain to think about. Asset management is a domain to think about. Delivery is a domain to think about. Field force execution. Think of this as you’re now bringing in the language of the industry into this knowledge base so that if someone manages a power plant, they’ll be like, okay, what’s my library of things I need to look at? Tomorrow, that can then connect it to people who are innovating or providing these solutions.

One important gap in the middle is, okay, how valuable is it? each of these ideas when it comes to cost, when it comes to emissions. And the work’s not yet ready to be unveiled, but we are quite privileged to work with the Grail team and, of course, global experts to start to now quantify, both in terms of economic impact, but also in terms of direct emissions impact, what each of these applications could be worth. Because then our scarce resources and limited time can be focused on the most important problems. And I think that’s what’s coming up ahead, and I look forward to all of you pushing the boundary further, and it’s a privilege to be part of this.

Thank you.

Okay, I will kick off. So as a metaphor for the climate, we’re drastically running out of time, and I can see a clock ticking down in front of me. So I’m Rob. I’m from University College London. 200 years ago, University College London was founded with a… a purpose to drive… change to be impactful and to create useful knowledge. That’s really important for the climate because we no longer have the ability to let knowledge sit on the shelf when it comes to climate. So in 2026 at UCL, the way that we bring our community together is through what we call the Grand Challenges. These are a self -funded, cross -university way of tackling problems that are too complex for any one discipline.

Climate crisis at UCL sits alongside challenges like mental health and well -being and data -empowered societies, and they’re found in all 11 of UCL’s faculties, from engineering to health and arts and humanities. So where does AI come into this? Well, AI at UCL is not seen as a single discipline, but as an enabling layer embedded across the entire institution. It builds on our heritage in AI. We’ve got three Nobel Prizes, we’re the birthplace of Google DeepMind, we’ve got several Nobel Prizes, we’ve got the Nobel Prize for the companies all at unicorn valuations. Four quick examples at UCL at the moment. Starting at home we use our own campus as a living lab we’ve got sensor data from across our estate that forecasts energy demand and detects unusual patterns across UCL’s buildings and we turn that into insights for practical intervention.

Second example we’ve got our spin -out Carbon Re which uses deep reinforcement learning and digital twin optimization to cut fuel use and emissions in energy intensive processes like cement production. Third example is a partnership UCL Center for sustainability and real tech innovation is created in partnership with PGM real estate it links computer science to the built environment and accelerates AI enabled sustainability in the real estate it drives impact on the environment but also value for the real estate investors. And then we’ve got our digital innovation center which uses deep reinforcement learning and digital twin optimization to cut fuel use and emissions in energy intensive processes like cement production. Third example is a partnership UCL’s center for sustainability and real tech innovation is created in partnership with PGM real estate it links computer science to the built environment and accelerates AI enabled sustainability in the real estate it drives impact on the environment but also value for the real estate investors.

And fourth UCL Grand Challenges has supported an inclusive and AI tool that transforms satellite and drone imagery into accessible web -based sea ice classification that’s being used to support safer travel for Inuit communities. Aviation is another frontier for us. It’s a grand challenge in its own right. And in there, we are looking at short -term and long -term interventions. AI is used to create short -term interventions that drive down its impact on the climate, while engineering is undertaking long -term technology transform in electrification and hydrogen propulsion. And finally, for UCL, convening really matters. In April 2025, as Uday mentioned, UCL, along with GRAIL, hosted our International Summit on AI Solutions for Climate Change, exploring sectors like energy and the built environment, and moving from discussions and pilots to deployment and impact.

I’ll finish with a quick call to action, which is that the grand challenges created by the U.S. government have been the greatest challenge for us. We’ve had a lot of challenges in the past. We’ve had a lot of challenges in the past. We’ve had a lot of challenges in the past. We’ve had a lot of challenges in the past. We’ve had a lot of challenges in the past. We’ve had a lot of challenges in the past. We’ve had a lot of challenges in the past. We’ve had a lot of challenges in the past. We’ve had a lot of challenges in the past. We’ve had a lot of challenges in the past. We’ve had a lot of challenges in the past.

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Cheers, and we’re properly into Alex Ferguson overtime now. So hopefully not with the climate change, so I’ll try and leave a little bit of time for Uday. So I’m Adam Sobey, I’m from the Alan Turing Institute. This is the UK’s National AI Institute. We focus on five missions across environment, which is focused on environmental forecasting and climate change, on sustainability, on defence and security, on health, and on foundational research. And as the Director for Sustainability, obviously I think that’s the most important mission, and that’s why I’m here. But we believe that the time for action is now. is literally on fire. We saw fires in the US which have been linked heavily to climate change.

We are seeing droughts in India which is affecting the food and people’s lives. We’re seeing pollution in Southeast Asia which is affecting health. We cannot wait for new fuels for the energy transition to occur. We need to do something immediately starting today. And we believe that AI can play that role. We know this because we’ve, as a part of our institute, have applied AI and data science to shipping and reduced emissions by 18%. We have done this in buildings where we’ve improved HVAC optimisation to reduce emissions by 42%. And we’ve created an underground urban farm that works entirely off renewable energies in the UK, allowing us to grow crops without using any CO2. However, I think we’ve done some relatively impressive things for a relatively small institute.

We’ve done some really impressive things for a relatively small institute. We can’t do this alone. we realised that this is a global problem and the Sustainability Missions chief funder is Lloyd’s Register Foundation which is a global charity heavily focused on the global south and so we think that it’s really important that we work together both within the UK and outside of the UK to solve these problems and that’s why we’re really pleased to be part of Braille to look for global solutions to global problems so thank you very much

It’s a tribute to all our speakers that they managed to put extraordinary quality into this ridiculously short time frame. I’ll just end on three words. One word is that we have come through our work together to find hundreds of examples of opportunities where businesses for example can save money or increase revenues, improve their economic value while at the same time massively improving their emissions profiles on the mitigation side. On the adaptation side… to your points. There are many examples from Google to all of your institutions where these technologies are already being deployed to save lives at a big scale. And the last point I’d make, apart from I’d ask you to thank our speakers with a big round of applause, is first to say that again and again you’ve heard one theme coming out of this group, which is radical collaboration.

Work with us to make the difference that we all believe and know can be made through the application of AI solutions to climate change. So maybe we could give our speakers a round of applause. Thank you very, very much.