Microsoft and AI: A Sustainable Future at Stake

AI for a Sustainable Future: An Exploration by Microsoft

Technological advancements are developing at a breakneck pace, sometimes making it difficult to precisely define the future we are heading towards. In the episode titled The Shape of Things to Come, Doug Burger, head of Microsoft Research, teams up with experts from various backgrounds to examine the complex questions that artificial intelligence (AI) poses to technologists, policymakers, business leaders, and other stakeholders. The primary goal is to enhance the common understanding necessary to build a future where the transition to AI results in a net benefit.

In this episode, Doug Burger is joined by Amy Luers, head of sustainability science and innovation at Microsoft, and Ishai Menache, optimization researcher at Microsoft Research. Together, they explore how AI can both contribute to climate change and help address it, while emphasizing the importance of distinguishing hype from real data and understanding the true impact of AI. Although data centers account for only a small share of global emissions, their rapid growth raises concerns about local infrastructures. However, AI offers powerful tools to optimize complex systems and accelerate climate solutions. The discussion presents AI as a crucial yet double-edged technology that must be carefully guided to support a sustainable future.

Podcast The Shape of Things to Come

DOUG BURGER: Welcome to The Shape of Things to Come, a podcast from Microsoft Research. I’m your host, Doug Burger. In this series, we will explore the capabilities of AI, delve into the fundamentals, and reflect on how these capabilities will transform the world — for better and for worse.

In today’s podcast, I invite two experts to discuss the future of AI and sustainability. One, Amy Luers, is an expert in sustainability at the intersection of sustainability, technology, and science. The other, Ishai Menache, is a globally recognized expert in optimization.

As we reflect on how technology can optimize systems, we will discuss the possibility of AI helping to combat climate change and sustainability, as well as the challenges associated with AI. We will try to get to the heart of the matter, as this will determine the shape of things to come.

I’m really excited to welcome our two distinguished guests today. We have Amy Luers, who is the Senior Global Director of Sustainability Science and Innovation at Microsoft, and Ishai Menache, who is a researcher at Microsoft Research.

The topic, of course, is AI and climate and sustainability, which concerns many people. We are facing a climate crisis. I have been a climate advocate since the 1990s. It’s something that worries me greatly. Amy has, of course, dedicated her career to this, so I can’t really complain, but it’s a very important topic.

And now we have this transition to AI happening. In the tech industry, we are witnessing a significant development of massive computing systems, mega data centers. And there are many concerns around the world regarding the impact this could have on the climate.

Amy, we will also talk about local communities. I really wanted to examine the facts. What does this actually mean? What will the real impact be? Let’s separate data from hype and also talk about the upcoming opportunities, because I think there are things we can do, and that’s why we have Ishai here.

Maybe I’ll first pass the mic to Amy. Can you tell us a bit about your role at Microsoft and what brought you into this field, perhaps a bit of your story?

AMY LUERS: As you said, I lead sustainability science and innovation in Microsoft Corp’s sustainability team, which means I work with very smart people across the company, worldwide, at Microsoft Research, to shape and inform sustainability solutions for Microsoft but also for the world.

Part of this involves leading our strategy on AI and sustainability. I have worked on sustainability and climate my entire life. I’ve worked in the tech sector, I was previously at Google. I’ve also been at the White House, working at the intersection of the CTO’s office and environment, resources, and energy.

I also led an international research network based on the UN, focused on sustainability. In this context, after leaving Google, where I started to think about the power of computing and digital tools for transformation, I was led to work at this intersection. When I began leading the sustainability research network, Future Earth, I really felt the need to think about innovation and digital technologies in this field.

I would say that the sustainability science network at the time, about 10 years ago, was a bit reluctant to consider AI and technologies in this area. I launched a global initiative called Sustainability in the Digital Age, where I really brought together the digital technology and AI community. It was in Montreal, where there is a large AI community, and sustainability scientists from around the world. I began to think about the potentials, the risks, and I led a large international study to establish a research and innovation agenda in this field.

This really changed my approach, moving from "big computing can help" — which I was focused on at Google — to the role of AI and machine learning in this area.

BURGER: And, Ishai, you are a globally recognized expert in machine learning and optimization. You have published extensively. I think you are well-known in your research community. You have also had a significant impact on Microsoft. You have been published in Harvard Business Review.

So, you are a bit of a polymath and sometimes a bit intimidating to me.

ISHAI MENACHE: Yes. [LAUGHTER]

BURGER: I’d love to hear a bit about your background, just a short version of your story for our audience.

MENACHE: Yes. My background is actually in engineering. However, my graduate studies were, as you mentioned, in machine learning, reinforcement learning, and later distributed optimization, game theory, a bit more on the theoretical side.

My story is that when I was a postdoc at MIT [Massachusetts Institute of Technology], the cloud was on the rise, around 2009. I was fascinated by the cloud. My initial interest was actually in cloud economics and, you know, pricing. How to price the cloud. I discovered Microsoft Research because at that time, a new type of lab was opening near MIT, MSR New England. I was fascinated by the cloud and, you know, not only the economic aspects but more fundamentally, how to use resources more efficiently.

That’s what brought me to Microsoft Research in 2011. I was a consultant at MSR New England, but then I moved to Redmond in 2011 to join a lab called Extreme Computing Group that was actually dealing with the future of the cloud.

And if I may mention, Doug, you were also part of that, so…

BURGER: That’s true.

MENACHE: I’ve known you for a while.

And so, let’s say my approach was that there were many people focused on systems thinking about cloud infrastructure. And at the other extreme, there were theorists thinking about the next wave or innovating in the algorithm space.

But I think what was missing was a bit of a bridge between algorithms and cloud infrastructure. That’s where I found a very interesting niche for myself, and later for the group I founded in 2019.

BURGER: You recently announced a system called OptiMind. I made a post on LinkedIn about it because I was really excited.

Can you tell us what this system does, why it has attracted a lot of attention? What does this system do? Then we can delve a bit into optimization for our audience. But then we need to come back to AI.

MENACHE: Sure. So, taking a step back. What is optimization or mathematical optimization? Optimization or mathematical optimization is a way of using mathematics to make the best decisions when there are many choices and certain limitations.

And, you know, a bit more concretely, in every optimization problem, you first have a description of the problem you need to solve. Then you have a set of decisions that, in mathematical terms, are the variables. You have an objective. What is your goal? What are you trying to optimize?

This could be something you maximize, like revenue, but it could also be that you minimize costs, so there are different versions or different types of objectives. And then there are constraints, which means you can’t do whatever you want. There are certain limitations, like capacity constraints in the cloud or other factors you need to consider to arrive at the best possible decisions.

BURGER: So, maybe a simple example… just to be a bit light for a moment, I have a complex commute to work, and one day I discover that the route I usually take is blocked and that my brakes are really worn and I can only stop twice. So, your framework might be able to determine which route gets me there while saving the most fuel.

MENACHE: Exactly. That’s an example. And maybe you don’t want to pay tolls for some reason, so that limits…