ADSW 2025: Connecting innovation to sustainable progress

Speaker A: Brought to you by Reuters plus Content Studios sponsored by Masdar.

Speaker B: Hello and welcome to Future Energy Talks with me, Andrew Wilson. It's well understood now that AI and other new technologies offer huge potential to revolutionize sustainability. But AI particularly is also energy intense. We ask how these competing agendas can work together. Coming up next, AI uh is the hottest ticket in town. It promises to fix all our woes and to send man to Mars. But behind all the hype, there are real benefits it can tap into. What? When it comes to making the energy process more efficient and helping reduce pressure on the power grid. At the Abu Dhabi Sustainability Week from January 12th to 18th, 2025, AI tops the agenda. ADSW 2025 is a truly global platform for thought leaders to gather around meaningful dialogue and tangible change. While AI offers great potential, it's also heavily energy intensive. So how can we find the right balance for this technology, one which leverages innovation but also mitigates the added cost to the environment? Here to answer these questions and more is Lauren Woodman, CEO, uh, of DataKind, a global nonprofit which runs on a Data for Good manifesto and uses AI uh, not just to improve business, but also to tackle social change. Datakind is based in the US which is where Lauren joins me from now. Lauren, hello. Good to see you. Thanks very much indeed for joining us.

Speaker C: Thanks for having me.

Speaker B: So, first of all, obviously, we're here to talk about artificial intelligence, but before we do that, tell us a bit about datakind. What it is you do, what your mission is, what you're trying to achieve.

Speaker C: Yeah. Datakind is a nonprofit we've been around for almost 15 years that, since its beginning, has been really focused on using data science and now AI to tackle some of the world's toughest challenges. And we work everywhere, from housing loss to frontline health to economic opportunity and humanitarian response, and frankly, a little bit of everything in between. We think data science and artificial intelligence has huge potential to help us change some of the hardest problems we've been trying to deal with for the last 25, 30, 50 years. And we want to make sure that we are using all of the best tools to address those challenges.

Speaker B: Well, you were on a panel at the World Economic forum earlier in 2024, uh, talking about AI and the energy transition. How receptive did you think the international business community were to the prospect of artificial intelligence?

Speaker C: Well, I think the international business community, and frankly, probably the world at large, has really embraced artificial intelligence because of the potential that it has to accelerate business, to accelerate good, to impact our lives, make so many, so many day to day tasks, frankly, easier for all of us to work through. I think there is a healthy amount of skepticism and concern about some of the ethics and equity issues that AI uh raises. And those absolutely are legitimate and we have to address them. But I do think there is a real hope that technology, technology and specifically artificial intelligence can help us tackle some of these problems and solve some of the challenges that we've been trying to address for so long.

Speaker B: Well, certainly I remember a lot of the conversation at Davos in 2024 was about energy transition, about turning old technology into new and how one might subsidize or support or even give way to the other. Uh, what role has AI got in that, do you think?

Speaker C: Well, I think there's lots of different ways that artificial intelligence can help us through this energy transition that we know that we are facing. Like, we know that artificial intelligence, for example, can help us understand where to site wind and solar farms for the greatest efficiency. We know that AI can help us manage the demands, um, so that our data centers are using less energy at times when energy is needed elsewhere. We know that as the um, energy system becomes increasingly decentralized and there's greater interaction and greater demands from all sectors, that AI, uh, can help us balance that across and understand where efficiencies might be gained. And even on the research side, AI is going to help us identify new materials and new processes and new operational models that will allow us to make the most of the energy that we have and to do so hopefully in the most sustainable and climate friendly way possible.

Speaker B: I mean, is it a question of organizations being motivated and harnessing AI to help them with their goals? Or is it a question of AI actually being the motivator and showing people ways that they hadn't otherwise thought of?

Speaker C: Well, that's a great question, and I think it's one that data scientists and AI theoreticians and business folks everywhere are really struggling with in that we know that AI can help us immensely when it comes to automating routine tasks in, um, you know, helping us take some of the, you know, frankly, the, the more drudgery bit of the work or routine work that we do on a day to day basis, and that is part of all of our jobs. AI can certainly help us in those places. We know that, you know, things like editing papers or, you know, writing blog posts or even designing logos that, you know, AI gives us a place to start, you know, and can be a spark for that. At the same point in time, there's also things that we know with more advanced models from AI that are helping us to spark new ideas or can model and predict research outcomes for us that allow us to identify the most promising places to look in lots of different sectors. I think right now, most of the places that we're looking at or most of the places where we're using AI is really in the automation of routine tasks and identifying efficiencies and those types of things. But increasingly we see AI moving into these spaces where it will help us point more promising directions for research, more promising directions for drug discovery. It will allow us to, um, find some of the underlying science a little faster so that we can, uh, combine that with human knowledge and human ingenuity so that we get to outcomes that address all sorts of things, including the energy transition.

Speaker B: I mean, we can all imagine, can't we, a world where AI starts to catch on and kind of escalate and find itself homes in all sorts of new places. Also, the world is very aware now of these massive data banks that we have to store all the information we have so far and how much power they consume, and therefore how much power AI must be destined to consume as it grows. Uh, what are we going to do about that issue, do you think?

Speaker C: I think that's a huge challenge, and I think it's one where you see industry and the energy sector working together much more closely and where consumers of AI, users of AI are very attuned to the fact that every time that they interact with an AI model, that's an energy drain that has an impact. So what you see is, for example, the big tech giants really leaning into where are they going to get power, which they have to have in order to remain competitive, how are they going to do so efficiently? And they're trying lots of different ways to look at that. How are they going to make that energy that they are demanding sustainable for the long term? And you also see organizations saying, look, not everything needs a large language model. Sometimes small language models work, and that demands less energy. You, you see consumers saying, what are the energy demands? Because as we think about, we as a company, any proverbial company thinks about our footprint in the world, you also have to think about the usage of AI and bring that into that conversation. So now we want to design systems that utilize AI, but do so smartly in a way that is more sustainable and minimizes the impact on the grid.

Speaker B: It's a classic case as well, of government and private sector, uh, moving ahead of each other, one falling behind, one moving Ahead, People worry, don't they, that AI will become the province of the private sector and that governments will fall behind in terms of allowing it to happen, of regulation, of who's in charge and who's responsible. All these issues need to be at least considered because they'll eventually arrive on our doorstep along with the, uh, more refined technology, won't they?

Speaker C: They will. But I think one of the things that we've seen, and I frankly, have been encouraged by over the last couple of years of months, maybe the last two or three years, as AI has really burst onto the scene, is what you see are governments and policymakers and thought leaders and academicians and the private sector and civil society all really leaning into this question around AI. And I think we see that for a couple of reasons. One, AI is changing so rapidly. This is not the industrial revolution that is going to take three or four generations to really permeate throughout, um, all levels of society. What you see is AI moving very, very quickly. And we know that we have to stay ahead of, or at least stay on par with the technological changes from a regulatory and policymaking point of view. So that's different. That recognition that the technology is moving so quickly that we have to stay, um, at least close, um, in terms of our regulatory and policymaking regimes, and you see governments leaning into that. Two, I think we've learned from past technology revolutions that it's not enough to say, let's see where this goes and we can clean it up later. Um, I am not a social media expert by any stretch of the imagination, but you certainly see the conversations that are happening around what we thought were going to be the positives of social media and seeing some of the harms that we now know exist, maybe those could have been avoided. And we don't want to make the same mistakes twice. And so let's get. Policymakers are saying, let's get into this conversation now and be part of it. And then, last but not least, I think some of the things that you see businesses saying, look, we know consumers and individuals are concerned about AI because there's a lot of scary news out there. So how are we going to be on the front foot to make sure that the tools and the processes that we're using, the technologies that we're using, are trustworthy and transparent enough that build confidence with consumers and, and don't undermine what we're trying to accomplish?

Speaker B: You see, what the consumer often sees is AI being presented as being a global phenomenon, something that will be for the common good, for the Global good. But actually history shows us that global cooperation is a lot harder to organize than we might like and that national interest often tops that as being a priority for different, more powerful countries over smaller, weaker countries. How can we keep an eye on, on the issue of equity as far as AI is concerned? How can we make sure that it's persistently and consistently deployed for the common good rather than being something to give or lend a competitive advantage to one or other actor in the game?

Speaker C: Well, I think you're hitting on one of the most difficult questions that we're facing in the AI space right now. And as a nonprofit that is really focused on equity for many, many years and works in that sector right around, you know, how do we advance common good and social change is one of the questions that we really lean into. For the last couple of years I've chaired a, ah, Global Futures Council at the World Economic Forum on Data Equity because I think all of us in business and academia and civil society recognize that that question, the question of equity is really one that we have to address. And we know that it's not something that is going to be fixed overnight. I mean, the data, for example, upon which all of these large language models are built is largely data from the global North. It's largely in English from a gender perspective. It largely represents the thoughts of men in the Global north and in developed countries. And it doesn't really include the perspectives and thoughts and experiences of the global South. And for many developing and emerging economies, we can't go back in time and fix that, but we certainly can pay more attention to it in the future and, and make sure that when we are building systems and we are using systems to make decisions, we are asking the questions around whether or not the data that we're using and the data that the models have been trained on actually represents the communities and individuals that will be impacted by the decisions that are being made. And I think we have to lean into the recognition that just because something is invented in one country or, or thought is a, uh, system is invented in one country, how do we make sure that those benefits and access to the outcomes are available globally? I think you've seen some of that work happening, um, at the un, the high level advisory board for the Secretary General that came together this year just released a report on AI and this was one of the big issues that they've highlighted. I think you see multilateral organizations leaning into this question and trying to find ways that we can use, um, development financing and cooperation in order to make sure. That those benefits are widespread and that knowledge is widespread. And I think you see a number of countries saying, look, if we've not paid attention to the digital revolution in a concerted enough way up until now, we really have to lean into this question if we're not going to get left behind, given the rapid change and the increasing rate of acceleration that we see in the technology space itself.

Speaker B: That kind of brings me back to my Davos question again. Really? You've been there many times discussing these exact issues with a group of business leaders whose agenda, at least for that short period of seven days, are up for discussing the common good rather than their own commercial interests. What was your sense speaking to or listening to others talking on this issue? What was your sense about how we can lean into creating a sense of equity around AI?

Speaker C: Uh, well, I think that everyone is trying to address that question. And you know, I work a lot with corporations and corporate social responsibility organizations and spend a lot of time working with corporations around technology and support of many of the projects that we do around the world. And I think that corporations really do want to solve this question. I think they also have sometimes competing commercial interest. But I also think that they recognize that their commercial interests are not just in their country or origin or in the countries in which they're already working. There's a whole market out there, a big giant globe where if they can get some of these problems right, they may have increased commercial interest. That doesn't mean they always get it right. That doesn't mean that sometimes there aren't conflicts between their priorities. But I do think that when I look at the partnerships that we've had, I see companies leaning into this question and being willing to work with a diverse group of stakeholders in lots of different sectors and in lots of different geographies to learn more, to understand more, and to share what they know for the benefits of the communities that these respective organizations are serving.

Speaker B: So we know, taking it back to the energy question again, we know that fossil fuel producers and uh, technology based around fossil fuels has had a troublesome year over the past 10 years. It's not necessarily the go to investment that it used to be. There is a wind of change blowing through that sector. So the embracing of AI, would that be a win win in terms of redirecting the energies of the big energy producers?

Speaker C: You know, I don't, I don't, I will not pretend to speak for all of the big energy producers, but I'll tell you that as you know, somebody who's been in the technology space for a long time. I look at that transition and I think, okay, if I, if I were thinking about that transition, what are the, where are the places that I would be looking and the things that I would be looking to is knowing that we need to move to more sustainable energy, um, sources. How do I do that? Where do I go? What communities can I serve? How do I take some old systems offline and bring new systems up? What are the efficiency trade offs, um, that are going to exist in that? How do I model that from m, a business perspective so I know where I can make investments and when. All of those are places where AI can help us make better business decisions so that companies can feel confident in making that shift. And as those opportunities arise, the other thing I would be looking at is how do I make my existing systems more efficient? You know, as we deal with climate change and the wear and tear on the infrastructure around the world, how do I predict those things more effectively and understand how I can protect, um, the infrastructure that I have in the systems that I already have? How can I use AI to, um, balance demand, make sure that I am sharing the load across the systems that I run? And is there a way for me to do that so that I am more efficiently, um, using the systems that I have and taking advantage of sustainable resources where I already have them in place? And then lastly, how do I look at my customer base and think about where can I use AI to identify opportunities for new infrastructure investment that can be more sustainable and how do I look at that from an equity perspective to make sure that all of the customers and communities that I'm serving are, are actually being given opportunities, uh, equal opportunities to be more sustainable and lower their carbon footprint.

Speaker B: It's fascinating hearing what you've got to say. If I may, for a final thought, then, Lauren, uh, ask you to look at your crystal ball and say, ok, how do you think things are going to map out in, say, the next five years? What kind of world do you think we'll be all working in in five years time when this stuff really starts to take hold?

Speaker C: Well, I've listened to a lot of folks in their respective crystal balls and I have a tremendous amount of respect for, you know, some pretty deep thinkers out there. I am not of the camp that thinks that work is going to go away and we're all going to be sitting on a, uh-huh. You know, in a lounge chair somewhere in five years and we don't have to work anymore because AI is doing it for us. Nor do I think that, you know, we are going to have massive displacement and, you know, horrible economic outcomes because AI has taken the work away from us. I think it's probably somewhere in between. I think what you'll see is a reordering of a lot of work in traditional roles that we see right now. I think you'll see some of the more mundane and routine tasks being taken over by AI. But you still need humans for creativity and to check and to deploy and to frankly inspire and push AI in the right direction. So if I look forward five years, what I hope we will see is that you will see a lot of jobs that have been AI enabled and AI helps them do their work more efficiently and really concentrate on the things that make work fulfilling. And I'll give you a couple of examples. We see folks, for example, in the nonprofit space, a client comes in, they're a direct benefit organization. You come in and need assistance. I could spend my time as a nonprofit worker looking through and figuring out which government programs or which nonprofit programs or which community programs might be best suited for you and whatever your particular state of need is. The other thing I could do is I could plug that into a computer. I could let AI come out and give me a list of recommendations while I deal with the human to human contact that is so necessary in many of those positions. We see that happening in community organizations and refugee organizations in times of humanitarian crisis. It allows people to work with people because AI is doing a lot of the mundane tasks on the back end and often finds opportunities that a human might have missed. We see that a lot and I hope that that's the way that work continues to evolve so that we can all get back to the things that we really love and let the mundane tasks be dealt with by AI.

Speaker B: And with that mundanity, do you think the technology advances as well will be in our interests?

Speaker C: Well, I think that depends on how we shape it. Right. Technology is a tool. What we do with it is up to us.

Speaker B: Lauren, thanks very much indeed. Fascinating talking to you. Thanks for joining us. Well, AI certainly offers hope that we'll be able to reach an energy transition which is both sustainable and fair. But it's still a complex solution to a complex problem. But as Lauren was saying, there's potential there with plenty more to discuss and think about. Which is why, of course, you need to check out Abu, uh, Dhabi Sustainability Week from January 12th to 18th, 2025. I'm Andrew Wilson and this is Future Energy Talks. Streaming now,

Speaker A: Brought to you by Reuters. Plus content studios sponsored by Mazda.