Pioneers of AI: Reid Hoffman on AI’s environmental risks and rewards
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Hey, folks. Our very own Reid Hoffman had an absolutely fascinating conversation recently with Rana El-Khoyyubi on our podcast, Pioneers of AI, and we just knew you would love it. It's the kind of optimistic, insightful, and nuanced approach to talking about artificial intelligence that you can find each and every week on Pioneers of AI. In this episode, Reid and Rana discuss how to be conscious about the environmental impact of today's energy-hungry AI tools and

and their hopes that AI could help unlock new solutions to climate change. If you haven't yet, I really hope you will follow and subscribe to that show wherever you listen to Masters of Scale. The world feels pretty chaotic right now, whether you're looking at global politics, the state of the economy, or doom scrolling on social media. And sometimes it feels like we're just standing on a beach watching as the tsunami is about to crash. It can feel hopeless.

But in these moments, Reid Hoffman has a different mindset than most. And it's something I really admire about him. I think that the entrepreneurial thing, the thing that you and I do and everyone else does, you always look at, okay, well, in crises, how do you make opportunity?

because that's the way that we make progress. And so I think part of the thing for thinking about this as citizens is to say, okay, the tsunami is going to, you know, essentially crush our coastal town.

what kinds of things we should be doing. Like how should we be preparing to rebuild? What kind of technology should we be doing? How do I make something as good from this as possible? And I think that's the right human and forward approach. It's a hopeful outlook and one that I strongly agree with, which is why I'm so excited to have Reid on the podcast today. Yes, he's co-founder of LinkedIn and one of the lead investors of our time.

But he's also a deep thinker who's provided me with a lot of guidance over the years. In April, we celebrate Earth Day, and the whole month is about sustainable living. So in this moment of uncertainty, which includes looming climate disasters, I wanted to bring Reid on to give us a dose of pragmatic hope. We talk about the concerns people have about AI and the environment, the

the push for more sustainable energy sources, and why Reid thinks AI could be the platinum bullet to solving climate change. I'm Rana El-Khalioubi, and this is Pioneers of AI, a podcast taking you behind the scenes of the AI revolution. ♪

Hi, Reid. Thanks so much for joining us on Pioneers of AI. Always a pleasure. Great to be here. Well, so it's been a hot second since we last connected. And in the meantime, you've written a book called Super Agency. Congratulations. And your whole thesis or premise for the book is that AI can amplify, not reduce our agency. So tell us more.

So, one of the things that I realized, there's obviously a whole bunch of negative discourse around AI. There's fears about privacy, there's fears about jobs, there's fears about democracy and all the rest. And I think all of this

kind of resolves at its core to a fear of human agency. And one of the things that was interesting when I looked at this is that this is the history of human societies confronting new technologies. It goes all the way back to the written word and the printing press, but also is, you know, cars and electricity and mainframes and all the rest, which is this technology may be the destruction of human agency, human society. Right.

And so this is not new that we have this concern in terms of what we're doing. And of course, in each time you look back, you know, across the, you know, kind of thousands of years of human society, the technology actually increases human agency. It transforms it. We lose some, but we gain lots more.

So there are so many things we can talk about, you know, especially related to AI's impact on society in the future. But for today, I want to focus our conversation on AI's impact on the environment. You, like me, are a big believer in the positive potential of AI.

But I guess I want to begin our conversation by addressing some of the real concerns that people have around using AI, and in particular, the amount of energy AI consumes. And I'll just share a few data points. So according to the Department of Energy, data centers account for about 4.4% of the total electricity use in the U.S., but that's expected to go up as demand increases.

demand and adoption of AI increases. So by 2028, that number could go up to even like 6.7 to 12%. There are a lot of people who are concerned about this. Do you think these concerns are valid? And if so, how do we address them?

Well, I mean, look, baseline concern is a valid one to say, hey, we're going to be creating this new world of intelligence. It's going to use a lot of electricity and training and having computational agents everywhere. Energy is one of the things that, you know, when it has non-green energy, it contributes to creation of carbon and other kinds of things. And as I say,

And you're telling me it's going to be an exponential increase in energy? Is that going to be even off this initial small percentage? Is that going to be an exponential increase in carbon creation? Is that going to have a climate impact? So that's not an unreasonable thought. But then when you start actually looking at the details of it, you realize the story is quite the reverse. First is...

all of the hyperscalers, Microsoft, Google, Amazon, are all making very strong commitments to make their new data centers green energy. And this isn't just, well, okay, so when it moves from 4% data centers to 8% data centers, the next 4% will be carbon kind of completely neutral. That's also great too. But in fact, all

All of them are doing essentially venture capital on green energy, which is like geothermal, hydro, solar, et cetera. They're all investing in new kind of energy companies that the whole challenge with energy companies is they have to get to scale in order to be cost effective and competitive in the market against global.

coal and against regular gas. And once they get there, then obviously that becomes energy that's available for all of the other purposes. Like even if you kind of say, all right, well, data centers are 4% and then 8%, you go, well, that leaves 96%, 92%.

There's all the other things we do. Can we make those green as well? And so this AI revolution, this data center revolution is funding the venture capital for making clean energy across all this. That's observation number one. Observation number two is the questions around, well, once you apply intelligence to things –

That intelligence can actually, in fact – it makes everything more efficient to what the intelligence is applied to. And so it's like, well, let's be applying kind of AI and intelligence to climate and to energy. So now it ranges everything from – call it the mundane, which is –

Hey, can you be running your household much more effectively? Like when do you turn off the heat or the air conditioning? And when do you turn on, do you turn it on just enough? Are you monitoring how it works? You know, similar across all the different electricity lights and everything else in the household.

to actually, in fact, your grid, right? And we've already seen with like DeepMind applying it to some of the most efficient grids, Google computing centers. So you can apply AI to all of these things that say, make the energy much more efficient. And then the very last piece of it, I think, is there's some hope that as we get AI better and better, it can actually affect

Right.

All of these other things – and it doesn't even have to be super genius AI creates fusion. It could simply be AI helping the people who are building fusion power operate more efficiently, effectively. Just think of it like giving them a Google search engine.

you know, to their work in order to improve. And so I'm a huge positive on AI being, you know, one of the many reasons for accelerating AI to actually helping in climate change.

So let's deconstruct all of that. So I first want to start with, okay, how can we create more sustainable AI, right? So that's kind of your reason number one. And I want to actually deconstruct it and kind of use the tech stack as a framework for how to think about this. So let's start at the very beginning, which is kind of the lowest layer of the stack, which is how are we, what kind of energy is needed to power these data centers and this idea of like cleaner energy. And in part,

In particular, I'm very curious about nuclear energy. I'm a tiny, tiny investor in a company called Deep Vision. They create these modular fission reactors that are a mile deep in the ground, so it's supposedly very safe. How do you think about nuclear energy and other forms of clean energy? And also, let's kind of talk about the regulatory process to bring these technologies to the world to actually be used to power our data centers. How far are we from that reality?

Well, the history of the nuclear technology side is kind of the classic microcosm of people being overly fearful and negative about technology in its early stage versus iterating to more positive. So for example, early in the US, we went, oh my god, nuclear can create radiation, can do these problems. Let's shut it all down. Let's not do any of this.

And you have Europe as a kind of case study, France especially, but where they said, well, no, let's continue to iterate it on, build on it. And then we've got our technology, which is like coal and oil and is massively creation of climate change. So all the environmentalists who are like anti-nuclear are

like actually in fact directly contributed to essentially the warming of the planet and climate change by saying not nuclear. And then of course they wanted not oil either, but like there's always, society says I must have energy. And then now we have the next generation of technology because I myself am also an investor in various fusion things, Pacific fusion and helium and so forth, which I do entirely out of a kind of a preemptive

human society way. I'm not an expert energy investor. I have no idea. Like you invest a dollar here and you make more dollars there. For me, it's a philanthropic investment.

And it isn't, by the way, only fusion. Fusion is always kind of the holy grail because it doesn't have the same radioactive components, but thorium and microreactors, you know, Okla and others are actually, in fact, all also really good. And then, of course, you also have things like the Bill Gates project with TerraPower, which is, hey, we know how to use nuclear byproducts to make them into the fuel that creates the electricity. And so it even gets rid of these things. And so

I think that the general lesson is amongst technology critics is not that technology can't be bad in some incarnations, but the question is always how do you shape it? There's obviously been, you know, you alluded to some of that. There's been some horrible, like really public nuclear disasters. How do we, yeah, how do we change public perception around nuclear energy and get the average person to be more excited about these kinds of approaches? Yeah.

Well, I think it's one of the responsibilities of leadership because, you know, part of how human beings naturally operate is that we tend to be, you know, kind of storytelling creatures. And so it's the same reason why, like when you see a plane crash, you go, oh, my God, planes are totally dangerous. Even though in almost every metric today, driving is far more dangerous than getting on a plane.

And yet people continue to be very nervous because they see a plane crash and they go, oh, plane crash and out of my control and dangerous. And so they respond the same way when you have a Fukushima, when you have a, you know, kind of some kind of

you know, kind of nuclear disaster. And by the way, it isn't to say like concern is not actually in fact bad, like old power plants are not great. And you actually have to, to, to, to kind of like, how do we build the new future power plants? Like the iterative development within technology is really, really key. And so now that being said, back to your question, um,

Part of the thing is to say, well, we're on top of this. We're navigating the concerns. And by the way, if we go and create a whole bunch of nuclear power, both fission and fusion, that makes a substantive difference here, that will make your life a whole lot better. That will obviate climate change. And that's where I think leadership comes in. Leadership is

helping people understand why it's important to do certain things, why it may be important to invest in something, you know, years in advance of it happening. You know, that's part of the reason why leadership goes, we need a bridge here. We're going to build a bridge. It's going to take years to build this bridge. But at the other side of the bridge, it'll be amazing. And that's the kind of thing that we have to do collectively as society's leaders. ♪

In a minute, we keep moving up the tech stack, looking at the ways AI interacts with our environment. Could innovations on the chip and model front lead us to a greener future? Stay with us. The Lobatical is for any employees who have been with us for five years to take a vacation. They get a week of extra PTO. They get to pick anywhere in the world that they want to travel, and we allow that to happen for them.

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So let's look at the next layer up the tech stack, the chips, right? So there is a lot of demand for both training AI, but also on the inference side. I mean, I fundamentally believe the whole chip landscape is very ripe for disruption. We've actually had a couple of innovators in this space on the podcast. One company, Etched,

They are doubling down on this thesis that transformer models are going to power AI. So they're building specialized chips for transformer models. And then, of course, Jonathan, CEO and founder of Grok, and they're really focused on the AI inference side. How do you look at the chip landscape and where do you think the opportunity is as it relates to more efficient hardware? Well, I definitely think it's once more you get this kind of the general principle of AI.

iteration of technology against a set of concerns that we as a society and markets are asking for.

you know, tends to get you some progress. And so I think on the chip side, this is part of the reason why we want this kind of technological innovation is because not only can you get energy efficient to the production of compute and intelligence, you might get a lot more production and compute and intelligence. There's a whole stack of things going on there. I think one of the things that we're seeing in the global landscape in the world right now is the the

the creation of compute infrastructure is the modern kind of cognitive industrial revolution, you know, kind of economic competitive race. Can you elaborate on that? Well, I mean, basically, if you say...

The next generation, which I think with AI, we're in the cognitive industrial revolution. And part of the reason I say cognitive industrial revolution is because it's both the amazement of what is our future economy and our future productivity kind of look like and

I think it's as or more impactful than the Industrial Revolution. And we don't have anything of our modern society without the Industrial Revolution. We don't have a middle class. We don't have broad spread education. We don't have broad spread medicine. We don't have all this stuff comes from the Industrial Revolution. Now, that's the upside of what we're moving towards.

Now, part of the reason I also use industrial revolution is because it's the – also the transition will be challenging. We as human beings don't do transitions in society well. It tends to amplify chaos and uncertainty. And so the industrial revolution, example of that, and we're going to have to try to do it better, but it will still be difficult and painful as we're making it happen.

But part of the reason – like some people tend to say, well, we'll just slow it down. We'll just stop it. It's like, well, but in the 8 billion people in the human planet, other people aren't going to slow it down. So it's kind of the you adopt the industrial revolution and we won't. That story plays very badly. So you have to play it through. And that's I think the kind of competitive race we're in. And then that resolves a lot to compute Fabric.

Because when you get to a whole stack of things that makes AI kind of work, a bunch of it is major data centers with a lot of compute in it, both for the training and learning, also for the inference. And a bunch of that within those data centers is the chips that power it. And that's part of the reason why, you know, in the commercial competition, the Chips Act is kind of saying, hey, let's only provide chips for

to essentially our friends and partners in terms of how we're operating. Let's make that happen. And by the way, that's also, of course, important within the energy consumption and environment, but it's also important in like the raw production of compute is one of the things that powers the cognitive industrial revolution. And that cognitive industrial revolution will be almost all of the products and services of the future. So you need to be steering into that.

Yeah. And of course, that includes kind of it's not really the final layer, but like another important layer, which is the actual models. And I'll just give one example. There's an MIT startup called Liquid AI, and they've kind of invented or created this alternative architecture for models, which is liquid neural networks. And these are much more energy efficient. They consume a lot less data to train models.

Yann LeCun, who is, you know, the godfather of AI, he's been pretty kind of vocal that he doesn't really think transformer models. He actually thinks like that approach is going to be obsolete in the next few years. To me, all that solidifies or underscores that we are going to continue to need and see innovation on the models front. What's your point of view? Well, I think we're going to see a ton of innovation on this. I think that the

Most fundamental abstract way of looking at the AI revolution is how do we apply scale compute to learning systems? And the thought that we just happen to have the first kind of scale, you know, kind of probabilistic model with transformers and with, you know, kind of large language models happens to be the one true holy grail seems a little improbable. It is obviously amazing. And, you know, there's things that, of course,

have been created in the last decade that previously were unimaginable amongst any of the experts. And one of the things I find really funny about the exploration of transformers is kind of moves this computer science into kind of natural science because like no one still has really good theories about why these transformers work at that kind of scale.

That's part of the reason why there's dispute amongst intelligent people, like between Lacoon and all the people at OpenAI and Anthropic and et cetera, to say, actually, in fact, how far will this go? And that's because it's gone a lot farther than anyone would have really imagined 10 plus years ago. Now, I am a believer that it will continue to go.

that Transformers will continue to do magical things. But like believing that doesn't mean that there won't be other additional really amazing things. And actually, in fact, I do think that there is other new techniques coming out in AI and other things that will make a very big difference here. But obviously,

When we get to acceleration within the software layer, like the algorithms that kind of put this together, what is the shape of the learning algorithm and everything else, that's obviously one of the places where a huge amount of acceleration happens because it's the acceleration of the world through bits versus atoms. And bits can move just, you know, I mean, pun intended, forever.

light speed faster than atoms. And that's actually, in fact, one of the things I think, so I, you know, not surprising as you worked your way through the stack, we get to the, one of the greatest accelerant and most plastic areas that there will, we will also see intense work on, not just in the next decade, but the next years.

What do you think is the consumer's responsibility in all of this? You know, should we, I think I know the answer to this, but should we all be using AI less to be more cognizant of the environment? Or should we be really aware which tools from which companies we're using that are kind of, you know, greener or cleaner models or architectures? Well, yeah. What do you think is the role of the average consumer? Yeah.

Well, almost if you go to your current percentages that you started with, you say, look, data centers are 4% today and AI stuff is probably 10% or less of that in terms of the data centers.

Your choice of a consumer about making any real impact is not actually – in fact, you're not having any energetic impact today on this. And as charted out earlier, I actually think all of the progress on this is likely to make not only your AI usage more efficient, but everything else in energy usage across the world and across the kind of major energy uses much more efficient as well.

So I don't think you need to at all as a consumer try to steer your AI usage away from trying to be, as it were, energy efficient. What I do think that it's important you do as a consumer is there's two reasons I think it's – at least two reasons why I think it's really important to engage.

One of them is helping steer the technology to being more human. That as you engage with it and see it and say, hey, I really like this. This is great. I could use this for various things. This is kind of creating problems. I'm worried about this, et cetera. So to be providing that feedback, whether it's in things you post online, polls you vote on, I think that engagement is really important. The second part that's really important is

I think one of the under-described things of the cognitive industrial revolution is which societies, which industries, which companies are going to define the future of these products and services. Those are going to be the ones that are actually engaged in using it. And so I actually think part of the race is not just the AI development of the, you know, kind of the raw capabilities for building the new products and services and making intelligence available everywhere, but

but also human beings using it, deploying it, engaging with it in their own work, in their group's work, in their company's work, in their industry's work, in their society's work. And I think that engagement is also really important. And so part of what I tend to do when I'm talking to people is say, look, go start using it today. And one of the things I think the AI is already good enough is if you haven't found something where it's important to your work today in using it, then you haven't tried hard enough.

In a minute, why Reid thinks that AI will be the platinum bullet that solves climate change. We'll get to that and why talking to animals might be part of the solution. Stay with us.

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You have said that AI could be the platinum bullet to solving climate change. And we've already kind of tackled some of these, but I want to double click on a few of them. As you know, we've had Astra Teller at Masters of Scale Summit and on the podcast. And one of the big projects they're working on is how to use AI to solve the electric grid problem. Let's unpack that a bit. Like, what is the problem and how can AI help solve it?

So part of the thing with a grid is, you know, the energy grid is kind of what transforms energy and moves it all around and,

There's a lot of energy that gets, you know, kind of essentially wasted in grid transport and kind of inefficiencies in the grid. And so one of the things that we were talking about earlier is intelligence applied can make lots of things more efficient and it can make grids more efficient. And, you know, you might look at your house as a little mini grid. Like, do you need to heat all the rooms, air condition all the rooms, or can you predict them and do it in kind of just the right amount? Right.

you know, lights on and off, efficiency of use of everything from charging your car and devices to, you know, use of the washing machine and all the rest. That's like a mini grid that you could go, hey, if I can save money,

call it 30% of my power here, that's a massive savings, cost savings, everything else just by application of intelligence. And then of course you apply that to the world's grid, your city, your state, et cetera. And then everything from your house to your entire society is one of the things that just basic application of intelligence. This isn't like, hey, we invented new fission, new fusion, right?

like can have massive energy savings, massive cost savings, massive carbon protection. And, you know, knowing Astro and his smart work and Google X's smart work, that's the directions they're gesturing at. Yeah, absolutely. So you also invest in a project called the Earth Species Project, which I think they're so cool. Can you talk a little bit about what they do and how, what's their approach to using AI to combat climate change?

The Earth Species Project is essentially like one of the things that comes out of the large language models is translation. Now, apply that to the sentient animals. Apply that to whales and dolphins. Apply that to apes and chimpanzees. Apply that to corvids and crows and say, hey, what can we get in translation for these species? And they definitely already have some

pretty interesting results from a

The fact that, you know, these various creatures do engage in language more than your average human being thinks that they do. Yeah. And it's so fascinating because if you're able to have this kind of connection and interconnection with other species in the world, I think the thesis is that that builds a lot of empathy and compassion for the entire universe, right? And hopefully that's a very different and unique way to addressing climate change.

Yeah, that makes it look if you if you say, hey, we actually, in fact, have empathy towards other species other than ourselves, which is easier to have when you can have a conversation that may lead to a better caretaking stance on the entire globe, which would be obviously just amazing. Exactly.

So then let's tackle the last leg, which is AI is this technology that is accelerating and unlocking innovation in other industries, whether it's biology, scientific discoveries, material science, robotics. Where are you most excited about AI's opportunity to accelerate innovation?

Well, I'm excited across a number of different vectors. I mean, one of the things that I've done this year, as you know, is with Siddhartha Mukherjee. I co-founded a company called Manus AI, which is accelerating drug discovery because you basically say, well, what's one of the killers across all human groups, all ages, all races, all genders is cancer.

And part of it is because there's so many different kinds of cancer that there is no like, you know, one pill to cure all cancer. It's one of the places where the kind of AI and intelligence amplification can be massively beneficial. And so that's obviously one thing.

But obviously there's a bunch of others. It's like whether it's the creation of physical materials, whether it's all kinds of things in robotics, in terms of everything from manufacturing, all of these things can be really amazingly done. And so it's part of the reason why I'm so excited about what AI can mean for humanity. And one of the reasons why I put a little bit of energy into writing Super Agency.

Yeah, exactly. And one of the kind of tenets of this book that I find really fascinating is that it is a collaboration and a partnership between humans and AI, right? It's not this competition. It's really this partnership. But it does kind of pose a question that I like to ask all my guests on the show, which is if AI can be so intelligent, so smart, so creative, so efficient, so empathetic even, right? Because I've spent many, many years building emotion AI.

What then does it mean to be human in this age of AI? Well, I think that as we evolve technology, our concept of what it is to be human also evolves. It's part of the, you know, like in several different books, I've kind of described us as more homo technae than homo sapiens. And it's almost like the technology is what wraps into the thinking, but also everything else. I mean, it's everything from, you know, glasses, the world, the world we see, clothing, fire, you're like all.

All of these things evolve what it is to be human. Like, for example, if you were hunter-gatherer tribes before the village, before agriculture, you'd have a very different conception of what it is to be human. That once you go to, you know, the Aristotle, we're citizens of the polis. We exist within kind of cities and towns and villages. And I think that even in the cognitive side with the printing press, it's

People would describe the, oh my gosh, this is going to be the complete degradation of human capability because no longer is this really absolute important ability called memory absolutely front and center and the most important part of intelligence. And he's like, yep, actually, in fact, now part of the progress has been is that memory is important but not as important as it was a century and a millennia ago.

And so part of what we're going to be learning is to say, well, what's the right way that we dance with this technology? Like, for example, here's the simplest thing. Clearly, for decades, computers play chess better than human beings. We haven't stopped playing chess, right? There's still lots of exploration in it. There's still lots of watching of it. There's still lots of doing of it because we as human beings can still engage in all these things, even as we use technology.

the technology to help us even become better. And I think that's where it is across empathy. That's where it is across cognition. That's where it is across what it is to be human. And we need to be kind of steering towards that as part of embracing our more human future. Yeah, I love that. So my takeaway is that

How can we, each and every one of us, steer technology to become more human and amplify all of these human virtues, including empathy and, of course, agency? Yeah, exactly. Reid, this was wonderful. Thank you so much for joining us on the show. Until the next time. Until the next time, and may it be soon. Always a pleasure.

When we talk about AI and the environment, I think we need to hold two truths. One, that AI does consume energy, leading to CO2 emissions as well as other environmental impacts. And two, that innovations in AI could lead to climate solutions. It's too simplistic to position AI as a net positive or net negative when it comes to climate. We may be watching a tsunami approaching the beach, but we still have time to act.

If we invest in new, more sustainable energy sources, innovate across the AI tech stack to build AI that is greener, and continue developing AI technologies that reshape our relationship with our environment, I'm hopeful that we'll see change. And before you go, a big thank you for listening to this podcast. It's a labor of love, and we'd love to hear what you think. Rate us and leave us a review wherever you're listening. Your input means a lot.

Next week on the Pioneers of AI feed, the team tries to uncover the energy footprint of an AI prompt. In other words, how much water, how much power, how much energy are you draining every time you ask chat GPT or inflection or Claude or whatever it is you're using to just draft an email for you? They follow each step of the process from the moment you type in your prompt and hit return. You don't want to miss it.

Pioneers of AI is a WaitWhat original. Our executive producer is Eve Tro. Our producer is Rachel Ishikawa. And our associate producer is Jordan Smart. Our senior talent executive is Stephanie Stern. Mixing and mastering by Ryan Pugh. Original music by Ryan Holiday. And our head of podcasts is Lital Mulat.

You can join the conversation on LinkedIn, Instagram, TikTok, YouTube, and X. Just search for at Pioneers of AI. Thanks so much for listening.