I want something to do with what I want to do with you.
I want something to do with you.
I want something to do with you. If you have to, you can use your hair.
“You know, you have to take care of yourself.”
And all those who work with me. And I want others to love what I want to do for you. I want to stay calm. Of course, I want two. But no, no, no, two.
No, I want to do something for you. I don't know what to do with you. There's also a way to do it. We want to do a career with you. That's Hanberg.
I want something to do with what I want to do with you. I want something to do with what I want to do with you. And if you have to, you have to work with me. And I want others to love what I want to do for you. I want to stay calm.
Of course, I want two. But no, no, no, two.
No, I want to do something for you.
I don't know what to do with you. I want to do something in Hanberg. I want to make a career with you, Hanberg. This is the AI Energy and Climate Podcast. In 2006, Lord Nicholas Stern released a landmark report
for the British government on the economics of climate change. The report, which came to be known as the Stern Review, argued that the economic costs of inaction on climate change far outweigh the costs of action. The report's been very influential, shaping policies
and not just in the UK, but around the world. A few months ago, Lord Stern, a professor and chair of the grant and research institute at the London School of Economics, released a new book called The Growth Story of the 21st century,
the economics and opportunity of climate action. The book examines the drivers of economic growth
in the decades ahead and argues that a well-structured transition
to a more sustainable economy will both produce greenhouse gas emissions and set the world on a path to prosperity and development.
“I think the book's destined to become a classic”
in the literature on climate change as well. And it can be downloaded for free from the website of the London School of Economics Press. And meanwhile, Lord Stern has also been working on topics very central to this podcast.
The role of artificial intelligence in the energy system in climate change. Lord Stern, his fellow economist, doctor, Mattiah Romani, a partner at systemic and several other colleagues,
released an important paper in nature last year exploring the potential for AI to reduce emissions of greenhouse gases in the years ahead. The paper includes some rigorous economic modeling and concludes, and I quote,
"The case for using AI for the climate transition is not only strong, but imperative." I was delighted to have a chance to discuss these topics with both for its stern and Dr. Romani recently. I hope you enjoy our conversation.
- Lord Nicholas Stern and Dr. Mattiah Romani.
“Thank you for joining us on the AI energy and climate podcast.”
I know you're both very busy. We're thrilled that you're able to join us today. - It's very nice to be with you David. I'm of course, Nick, as you know, and it's a pleasure to be here alongside my good friend
and collaborates in Mattiah. Great to be here. Thanks for having us, David. - Well, Nick, I'd like to start with you because you have a new book out.
The book is called The Growth Story of the 21st Century. And it's a tour de force. It covers the fundamentals of climate science, economics, and policy. You're analysis of the ways that climate action
can contribute to economic growth. You talk about domestic and international policies. Maybe just to start us off, could you summarize the argument of your book? - Yep, it's embodied in the subtitle.
The book, which is the economics, and opportunity of climate action. So it's essentially argues that the investments that we have to do in the structural and systemic change that we have to undergo will be the driver
of the new growth story. And that it's an investment story. Par excellence, quite a lot of investment, of course in physical capital, but not just physical capital, but also human natural and indeed social capital
in terms of the way in which we work together, collaborate the institutions function and so on. So that's the story. The story of climate action driving growth within that story, of course, it's an intersection
between AI and the new technologies. The new growth story will not be driven by the technologies of the 19th century. It'll be driven by the technologies of the 21st century. Of course, the wonderful supplies of clean energy
that we have, the different ways that we have of organising cities and our land and our energy and our transport. And particularly in those systems,
Managing those systems, changing those systems, AI
will be very important. And I offer six drivers of growth, and let me give those six and then stop. Essentially, clearly, sheep and the dirty across so much of what we do.
And of course, also innovation. Secondly, separately, conceptually, but interwoven is the calmness of scale. So much of what we're building up, particularly because much of it is systems infrastructure
has got quite strong increasing returns to scale. We've also seen increasing returns to scale in solar and so on, as well.
“A lot of it, and here AI will be very important,”
is around efficiency. Greater efficiency, less waste, greater efficiency. Energy, of course, particularly, but across the board is greater productivity is better growth. So that's a part of the story.
The whole function of systems, I've mentioned it already, but that's one of the key six factors. Cities, land, transport, energy, water, managing systems, indeed, changing systems. AI has got tremendous amount to offer.
There's a big story of health. The dirty is dirty, and it kills people, and it mames people. And dead people are not productive, and the same people are less productive.
I hope we care about killing and maiming first,
but there's a clear productivity story as well. And finally, to get this done,
“we're going to need a lot of investment.”
Two to three percentage points of GDP across the world, 45% in some developing countries. That investment will drive growth. It's also a story of adaptation within that story. If adaptation shows very high returns, 10, 15% or more,
in terms of internal race return, or very big benefit cost ratios, and so on, if you do it, that way, a very high return project, finance on longer term, moderate cost capital, boost growth, that's what we mean.
So adaptation, big part of the story. So in order to do this, you've got to invest a lot, but it's a fantastic opportunity. I'm enormously optimistic about what we can do, and I'm very worried about what we will do.
We have the world has the ability to miss opportunities. - You mentioned in the book an appeal to economists, and I thought there was almost interesting sections of the book, and maybe just say a word about your appeal to your fellow economists.
- Well, back in the day, and it's still reflected in what economists do, but happily a bit less so. You modeled a problem of climate change and policy towards climate change in this way.
You say there's a basic underlying growth model that's carrying on, going on. Now, underlying growth rate of growth, 1% or whatever it might be. And the climate comes along,
we've never heard of it before, now you've heard of it,
and it comes along and it perturbs, and it can give you downside impacts because the way it works, you know, whether you got the slow on-sets and the desertification, and you've got the extreme weather events,
and so on, and fundamental changes in the Amazon, and the Antarctica and the Arctic story, and the Himalayan and right across. So these effects of climate come along and they perturbed this nice steady growth path somewhat.
And you might lose, say, five degrees centigrade, you might lose 10% of GDP. Well, if you write down a model like that, you're not gonna do very much about climate change, because five degrees centigrade might come at the end of the century,
when you're quite a lot better off, because you've assumed and underlying growth, why would you buzz it too much, yeah?
“And lo and behold, that's what the models tell you,”
and you know, Bill Nordhouse came out with the optimum temperature being 3.5 degrees centigrade. But you can see where it comes from, right? If you have underlying growth, so you're gonna get richer anyway,
and the effects are pretty modest. Why would you get so excited about climate change? And so the basically, those models, misrepresenting the problem,
it's not simply that models always misrepresent the problem.
Is that the models lose the essence of the problem, because climate change could disrupt and reverse growth, and the effects could be absolutely catastrophic
For hundreds of millions or billions of people,
whole sections of the world would have to move.
And it also misses the point in terms of the response, because the response is fundamental systemic and structural change. And that is not in aggregate growth models. So not only misrepresent the problem, you also exclude the key elements of the response to the problem.
So, you know, I sometimes quote in the bank of England in the UK, they're very moderate in their language, yeah, all decent middle-class people who have to be very careful, they say.
The strongest language they're allowed to use is unhelpful.
And I see these models have been unhelpful.
“To stay up to date on all the news that you need to know,”
there's no better place than right here on the DSR network. And there's no better way to enjoy the DSR network than by becoming a member. Members enjoying ad-free listening experience, access to our discord community, exclusive content,
early episode access, and more. Use code DSR 26 for a 25% off discount on sign up at the DSR network.com.
That's code DSR 26 at the DSR network.com/by.
Thank you, and enjoy the show. Amazon beat it all in fresh-gabaktenen-Eltern in the logistics center, extra family, so we're going on. The city is a new city in the city. Your collection is for the beautiful city of the world.
That means, it's like the most beautiful city of all. One of the parts I appreciate about the book a lot is that in a number of places you provide rejoiners to some of the standard arguments against climate action. And so I really-- I highly recommend this book to all our listeners.
“I think it's destined to be a classic in the climate literature.”
It already is a classic in the climate literature. You lay out a lot of the basics. You lay out courts, eerie, and you provide rejoiners. So thank you for that. David, you could just let me allow me to say that you can download it
absolutely free of charge because it's published with LSE press, which is open access. Fantastic, and thank you for that as well. So we've-- artificial intelligence throughout your summary of the different steps that are needed to address climate change. And I want to turn to that topic now.
And you and Mattay wrote a very important article.
“I think it published in Nature, wrote it along with a number of your colleagues.”
On using AI tools to fight climate change, and Mattay, I want to turn to you. And maybe just started a high-level rescue to summarize the basic argument of that article. Now with pleasure David, and it's actually very-- it's a very natural continuation on NICS argument about the need to look at this issue's system wide, and to see what is the system impact of changes, both in terms of the risks, but also of the opportunities of the response.
And that's really what we try to do with this paper. We look at AI, and we ask ourselves, OK, well, AI is talk about primarily-- because of its additional energy consumption, and emissions associated with it, and its benefits are primarily talked about in terms of selling you more things on Instagram. So we ask ourselves, well, if it is indeed the general purpose technology, what if we apply
this general purpose technology to accelerate the low carbon transition, what would be the potential that AI could have for that? And we do that by looking at three sectors only, just to be conservative and to try to do an assessment, which is really top down to try to figure out the magnitudes of the potential. So we just look at power, food, and mobility as three sectors, and we ask ourselves, well,
given the technologies that we see are transforming the sectors, we work to apply AI to this technology. How much faster could we adopt them? And if we were to adopt them faster, thanks to AI, what would be the consequences for emissions? And what we find is that if we apply AI intentionally to these three sectors, and we can speak a little bit more, indeed, and later on what we mean by
Applying AI intentionally to these three sectors, but if we were to apply AI ...
three sectors, then AI could reduce global emissions. We estimate by between, say, 3.25.4, gigatons, CO2 equivalent per year by 2035. So 3.2 to 5.4 is a very large number. Just to give you a sense, it's about halfway or where we would need to be to be on a net zero trajectory by 2035. So it would really make a massive contribution of getting us on the right track for full transition. We also then ask ourselves, well, is this true even if AI admits quite a lot
because of its power consumption, the answer is very much yes, even taking the most conservative
assumptions on AI continuing to use the same sort of power it does now without massive innovations on the reductions of power consumption from AI, and taking a very, very conservative look at the kind of emissions associated with the additional power by looking at simply that AI accessing power, if you want the average carbon intensity of the power, we find that additional emissions from AI would be a greatly awaited by the potential for emission reductions applying AI intentionally
on this technologies. So that's the big message from the paper. It's a very important bottom line
message and this is a peer-reviewed paper in nature. Maybe just to follow up, let's start with
you mentality. What are some examples of ways in which you think AI can reduce emissions that you find most interesting or exciting and then maybe turn to Nick with the same question?
“Well, look first of all, I think the most important thing to understand is that it is a GPT”
a general purpose technology. So this is not just making a gadget work better or making a specific bit of technology more efficient. This is really about transforming systems and that's what that paper really emphasizes, stability to optimize complex systems. And if you think about the complex systems, we have there you have your examples of the mass astonishing places where AI can really have great potential. So for example, optimization of energy grid and power system,
grid balancing for renewable, forecasting demand for power. And they're providing a better optimization of web power should come from in the system and they're maximizing stability
for power to come from the greenest, which happens to always be the cheapest sources is very,
“very important. Optimizing energy storage in that context as any of the storage becomes more and more”
available. That to me is a really primary example of how AI could achieve some of the potential to relay out in the paper. Nick, are there examples that you think are particularly compelling and exciting? My dear game, very clearly the energy example and that's enormously important. But also the whole story for example of cities and transport. We are facing now a world where urbanization is moving very quickly, particularly in the developing world, cities are growing
very rapidly. How those cities are designed, created, structured over this next 10 or 20 years will be a fundamental importance to how they function thereafter. The echoes of city design and city planning go over the centuries or even the millennia. A lot of I'm in London where I
“ball him grew up and our roads, many of them, but not of course very far more with some of the key”
rows of Roman roads. And underground what we call the underground were other people called the Metro, the underground railway system, some of those follow those roads. Those echoes are a long go far into the future. How we design our cities and how they work around a world in the next 20 years is going to be fundamental importance. How the Metro's interact with the buses and the bikes and the walking. Those are very complicated, structural questions. It's not just running your
Uber within an existing and it's done brilliantly of course. It's how you design the whole system in which the Uber sits, with the knowledge that lots of people are going to be running Uber within it. So it is an extraordinary design problem and it's not just about optimizing systems that you've got. Of course you must make the best of whatever system that you've got. It's about designing
Good systems.
you know you're designing these things for a really long period of time. This podcast is underwritten
“in part by the US Embassy of the United Arab Emirates. It's editorial content. It's completely”
independent and the views expressed are exclusively those of participating experts. It is presented live without editing. For further information about the UAE's efforts in the areas of Artificial Intelligence and Technology, go to the website of the embassy at www.ue-emBC.org and search for UAE-US tech cooperation. We thank them for their support. We thank everybody who is supporting
this podcast for their support and we look forward to it developing and growing over time because
the issue is so important. I'm going to say thank you for all the questions. To all the prizes. This week, Mini Cherry Risk Potomatton 300g for 0.2€. 97 or tablebills, the kilo for 0.1€. 49. And many other answers to all the prizes. Now in your Al d'Nord, for years and further, with fresh music for every day, all of them. Good for everyone. I want to do something for you. I want to do something for you.
I want to do something for you. I want to do something for you. I want to do something for you and all who work with me. And I want to do something for you. I want to do something for you too. I want to do something for you. But no, no, no, I want to do something for you. I want to do something for you. I want to do something for you. Let's do it. We are going to do something for you. I want to do something for you. It's that AI can detect, predict, optimize and simulate. It can detect, predict, optimize and simulate.
In each of those kind of general purpose capabilities can be used in a variety of different ways
“to fight climate change. I think your paper does. It has as somewhat different but related”
typology for thinking about these problems and I really think it's extremely useful. I would add to the verbs that you listed and they are all important verbs. I would add design. Interesting. Could you elaborate on that? Yeah. I mean, you work a little bit. If you take what? How are we going to design a new city? How are we going to design the expansion of a city given what's already there? How are we going to design the road layout? How are we going
to design the way in which different methods of transport interact and exchange with each other? How are we going to manage the heating and the power systems in a way that recognize that there's a big system here with different parts going to affect the other parts in different ways? That's a design issue. We know from all the discussions around to very useful discussions around a circular economy. It's how you design the things that are going to work in the economy
in this case in a very focus way around reusing and recycling it and so on. This is a much bigger version of design than that. There's how you design the whole city system and the
“energy system and transport. The design side, I think, is enormously important. We want to”
whisper into chat, GBT, design me a sensible city. Can I add another verb to your list? I would add this cover and we talk about it in the paper and with just at the beginning of a journey of understanding how AI can help us discover new things and perhaps the journey has started with materials.
Just a very simple example, electrolytes. As we know, electrolytes are an absolutely essential part
of a battery and solid state batteries require electrolytes conduct the lithium ions efficiently while retaining the chemical stability and please bear with me as I'm an economist talking
Chemistry, so it's very, very approximate, but that's my level of understandi...
millions of possible material combinations and quest each of them could take decades at the
anonymous cost and what we saw over the last few years is that the researcher using machine learning models that are training on existing material databases have shortened the time so significantly. They train the model, feed it with the feed AI with thousands of no material characteristics and measure properties, then they screen through huge material libraries and that ranks potential candidates so that the researchers end up testing maybe a couple of
dozens candidates in the lab instead of thousands and that cuts down time from years to weeks and that's happening today and deep mind is probably the most famous place that has started doing that but many, many are now doing that and that's just the beginning of that journey of discovery. And since you're both a economist question, which I don't know the answer to, how does one model
a transformation like that and when there's a potential breakthrough of the type that we start
say with protein folding, where you could really have a dramatic breakthrough, for example, with battery chemistry, with fusion, perhaps something like that, how do you think about this and economists, and do the figures that you were cited from your modeling, do they reflect the potential
“for that type of breakthrough? I think the different ways of doing it, one thing in the way that”
material described is that AI could map out a range of possibilities in a way that would be more imaginative than you could do just by yourself because it would cover such a big range and it could also probably delete some of them, which can be found to be not working very well relative to whatever criteria or efficiency or whatever you emissions, whatever it is, you pour on it. So I think we should see AI is generating possibilities in terms of a much more structured approach
to scenario analysis than saying think of small medium and a lot of scenario analysis is like small medium and large, and this is to looking at a much broader, broader way. So I think that is a very
important part of that story. The second is more abstract modeling, and Philippa Giorno, who just
got with Peter Howard and John Mocker, the Nobel Prize around Schumperterian creative destruction. And he's been working on ideas, it's fairly abstract at the moment, but a few numbers saying that if you just look at AI in terms of what we know about what it can do, you might conclude that the
“effects of AI on productivity would be very modest. And that's what Darren, as among glue, great”
economist, came up with, but Philip's arguing that if you use AI to look at the discovery process and the creative process in creative destruction, you get much bigger numbers about what it can do to the productivity story. So there's a two very different ways, now I'm sure there are many more, but those are two very different ways in which AI illuminates the whole process of system change, discovery, and so on. And we're going to find out a lot more.
That's reminds me of a somewhat related point you made in your paper. You talk about AI being able to contribute to moving beyond GDP, like measures for economic, for economic, and I've long bomoned the dominance that GDP has in our political dialogues. I remember reading years ago that the cancer patient getting a divorce is contributing enormously to GDP, but obviously not to happiness and utility, and there's a lot of shortcomings with GDP as a measure and talking
“your paper about how AI can contribute to moving beyond GDP as a metric. Any thoughts about that?”
Let me try, because I haven't really pursued that, but so much of well-being is about relationships, and how people interact with each other. And that's already another order of computational magnitude, and if you just look at individuals, if you look at the relationship between individuals, you know, your upper degree on an infinity, as it will, in where you look at those. So I would
Hope that AI, I mean, I don't mean that AI can emote with us, or if I'm not p...
on emoting with AI, but if it can explore different patterns of relationships and what affects that
“they have and how they affect behaviour with that, then I think that's very, and it could be between”
old people and young people, you know, could be between the service, those who work in the service, you know, I'm not talking about necessarily marital relationships in all this, in relationships within the society, and we sort of lump that into social capital in many ways, and then I think it's something that I hope our anthropologists and sociologists and our psychologists will start to explore from the perspective of AI. I mean, for all I know they're already
doing it, they probably already are. But that seems to be an example where the complexity and the numbers might be too daunting without AI, but with AI, we might be able to get our hands around some of these issues. So I just want to know what's going on and all of these things work with me, and I want to make the other life what I do for you, I want to stay in love with you, I want to stay in love with you, of course, my two children, but not not to be in love with you. So I want to be really strong.
So I'm not going to be there, now I'm still in the hands of AI, we're going to do a career with you, that's the handbag. I want to know what's going to happen, what I want to do,
“where I'm going to get my father's handbag, and if I have to stay in love with you, that's why I'm”
trying to work with you, and I want to make the other life what I do for you, I want to stay in love with you, of course, my two children, but not not to be in love with you. No, I want to be really strong. So I'm not going to be in the handsbag. We're going to do a career with a career. That's handbag. It's fascinating, and Mattayah, before we leave a discussion of your of your terrific article in Nature, just any final thoughts about AI and how I can contribute to
greenhouse gas emissions from your paper? Well, David, of course, the thoughts is that I am absolutely positive about one thing about a paper, which is that the numbers you need to hold wrong, and that's a little bit the reason why we wrote a paper like that. We want to get the magnitudes right, and then we want to have a lot more work emerging from this paper to get those numbers right, and to understand how you actually realize, I mean, practice. So the agenda
“work that paper laid out, I think, is too full. One, we recognize modeling that we have is inadequate”
to capture the potential benefits of AI, and so we want to understand that a lot better and go a lot deeper around how the technologies can play out and in what way they can help us accelerate
the low carbon transition, and that future that makes book describes so compellingly. And second,
the second agenda work is how to create that intentionality in the application of AI for impact, and AI use it in a way that can really be a fundamental tool to take action on the largest crisis planet that's facing. Well, we're almost out of time, but Matti, I wanted to ask you about some of the UNI both attended a couple of weeks ago. We were both in Delhi at the India AI impact summit. I thought it was pretty remarkable that with, I think, 35,000 people, 20 heads of state,
tech leaders, including Sam Altland, Darryu Ahmeday, Demethasabas, any reflections on your part, what do you saw there in its implications? Well, I won't go on about the traffic, which I think everybody's talked about, really like me, because we didn't have to be meeting in various cars on the way, even out of the summit. But look, I mean, to me there were many things which are really interesting, but perhaps one that I thought was really fascinating is we did the math on the commitments
made at the summit. And I counted more or less, a quarter of a trillion dollars, about $250
billion was committed in public and private funds to pledged in terms of boosting AI and a lot
of it in India. And so we're talking about not just building data centered by research and deployment to ensure that these AI can have the impact that this summit laid out as it's objective. And I'm thinking about those numbers in the context of the numbers we often talk about, David and Nick and myself, about the need for investment in the climate transition and the numbers are pretty much that magnitude. And so it made me think that having the word pledge for action at scale
Is very possible, very possible.
that in main realized is that we are in an incredible, probably once in a lifetime of
opportunity of topics and investments across the world, global south and global north, we cannot miss that opportunity to make sure that that investment actually benefits everyone. It's a really once in life, that opportunity to ensure that money is an impact on climate, but it also has an impact on poverty inequality and the crisis that go together with climate. Well, thank you. Well, we close all our conversations around this, but that's with two questions
for each of our guests. And the first question is really simple. How are you using AI tools these
days? And maybe I'll start with you, Mataya, then, Eft Nick.
Well, not enough. I use General TVI quite a lot on chatchipity constantly. I use it in trees, not just to write things for me, but I use it to analyze complexes of data. So for example, last week I was holding a workshop and we asked people to rent a set of options on impact and feasibility and they rent about 200 options on this big board. And all I did is I took a picture uploading chatchipity and asked them to give me a sense of what were the top options and how
what percentage of distribution you had, something that would have taken me a long time to do it manually. So I'm getting to start using it analytically, but not enough. I'm still stuck on
“General TVI. It's hard to keep up with all the advances. Yeah, Nick, how about you?”
I'm much less advanced than you David and Mataya. I'll give you an example. I was in a meeting actually in the House of Lords where I sit. When I knew that a report that I'd led 10 years ago on the reform of the research excellence framework for assessing working UK universities was relevant to the conversation. And I wanted to introduce it. But I couldn't remember exactly what I'd said 10 years ago. So I put in stern report on research excellence around 10 years ago.
And it came back with the four key points that we made within a few microseconds. Absolutely on the button. So I was instantly prepared. This whole thing probably took less than 30 seconds. So be able to introduce why that she said 10 years ago in a rather nicely structured way. And that's the kind of way in which I'm using it to quick synthesis of where we are on something. And I must say the test using my own particular work. It passed magnificently. But I'm not using it
“to generate or to write as yet. But I probably should. I still have this view that you have to write”
by thinking and encapsulating what you thought. And I have some books. I think you'll ask about books. So I've got some books that illustrate that. Well, please. Well, that's a great addition to our last question. Yeah. So our last question is to please recommend any reports or articles
older, new, uh, to our list. Okay. So next question. Excuse me. I'm going to, the first thing
are a pair. What I went to my PhD supervisor just just beginning as I changed from mathematics to economics. This was in 19, um, this would have been 1967, but I was very fortunate. My PhD supervisor at Cambridge was James Merleys who subsequently won the Nobel Prize. And I said, Jim, I'm changing as we both know from mathematics to economics. What, what do I read this summer? And he said, read Cambridge General Theory and he read and read Schumpatis, Capitalism, Socialism,
and Democracy. And I've gone on making that same recommendation to all my graduate students ever since. And it's a wonderful combination of systems approach. They both look at the whole structure. They think about the dynamics. Schumpatis are a bit more about the dynamics than, uh, okay, they think about the dynamics. And they each think of a world that's not necessarily an equilibrium. And, you know, and, you know, those are, those are essentially from the 30s and 40s.
“And if we had that kind of thinking, much more prevalent in economics now, I think we'd be doing”
much better. The best novel of all that I would recommend, um, obviously best is a very personal thing. Would be George Eliot's middle march. She gets relationships in a, in a wonderful way. She gets hypocrisy. She gets the bonkersness of academics. She's writing about a time when the railways
Are coming in in the UK.
how much is there. And if I'm allowed one more, and this one, people may not know about,
“it's a wonderful book by somebody called CLR James. And it's called Beyond The Boundary,”
published in 1963. And he writes, it's, it's apparently about initially about cricket. And he was a black man playing cricket at a modest level, including in West Indies and in the UK. And he writes about racism in cricket. He writes about the British social classes. He writes about how the West Indie and first became a black West Indian, first became captain of the West Indian team. And at the same time, he intervened with an extraordinary analysis. This was 1963
of Victorian society when sort of the most famous regional cricketer called W.G. Grace was active. And he intervened with the wonderful novel, you know, Vanity Fair by Gullion Makepiece Thakrey. It is quite absolute sort of force of sport politics, you know, colonialism, British society in the
19th century. It's just an amazing piece of work and it's a slightly cult thing, but those of
us who are involved in it get very, very enthusiastic. The tables beyond the boundary, beyond a boundary, beyond a boundary. You know, it's an iced up to do a long time. You know, the boundary is the boundary of the cricket pitch, but it's also the boundary of what you can do. And on cricket, on cricket itself, he's absolutely brilliant. So if you're a cricketing nerd, you'll love it as well as if you're not a cricketing nerd. Well, thanks. Oh, thanks for those.
Mattia, you have the last word. I'll be quick on my books. I mean, I should say that this is the
“only book I'm reading, which is mixed with this book. However, I remember. And for those listening,”
he's holding up the grocery at the 21st century. Yes. Indeed. But it does, there's another three books that I've been reading, which I've loved. One is the julio book collectees, the environmental
Republic, which just came out. And it's quite good because it basically is a book that makes the
argument that nature should be inside society, not outside society. We're used to being in the city and going out for the weekend. And that's our interaction with nature about. What does he mean to bring ecosystems as part of public infrastructure? And if you believe that they're part of public infrastructure, then managing them become a civic responsibility and responsibility for our political system and institutional systems. So it's about building a political self-covering
society that manages ecosystems as part of social contract with its people. It's very good and
I really recommend it. I'm just back from India and I always like to read a book about the
country that I go to. So I reread and I actually love the DSNIPL India, which is a great way of having a sense with a bit of humor or the country. And last but not least, I'm working a lot and thinking a lot about the future or multilateralism they get. And so I picked up an old book,
“which is the world's banker. I'm not sure if you remember, this is the, well, it's really the”
biography of Jim Wolfenstein, present the World Bank. And it's actually incredible how timely some of the sort of transformations that Jim Wolfenstein introducing the World Bank, are still today. And it really said me well to think about how to introduce some of this in the future, what we need from multilateral institutions. Well, thanks to both of you for those recommendations for joining us today and for all of your visionary work on these topics. We are not only
at time or overtime, and so let both of you go, thank you so much again for joining us on the AI Energy and Climate Podcast, and thank you very much for your time. Great talking to both of you, all the best. So what's going on?
