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As we navigate our paperless offices and admire our sleek compact computing devices, it can be hard to imagine the impact that our digital experiences are having on our communities and the planet.
Gerry McGovern studies the environmental impact of the digital industry. He has uncovered an alarming story of unsustainable growth, toxic side effects, and human misery, which he shares in his book, World Wide Waste.
We talked about:
- how he became an environmental activist focused on the impacts of digital
- the phenomenal pace of growth of digital infrastructure
- the impact on local communities of the big data centers that house cloud infrastructure
- how the compute-intensive nature of AI exacerbates
- his observation of the long-standing lack of transparency in the AI industry
- the “snake oil sales” aspects of AI
- the troubling use of “forever chemicals” by the semiconductor industry
- the material impact of computer chip manufacturing
- how human over-consumption and the environmental impacts of AI overlap
- his advice for actions you can take to mitigate your personal impact:
- slow down and use your brain more
- think local – local foods, local computer storage, etc.
- prefer text over images and other high-bandwidth communications
Gerry’s bio
Gerry’s latest book, World Wide Waste, examines the impact data waste and e-waste are having on the environment and what to do about it. Gerry also developed Top Tasks, a research method used by hundreds of organizations to help identify what truly matters.
Connect with Gerry online
Video
Here’s the video version of our conversation:
Podcast intro transcript
This is the Content and AI podcast, episode number 23. It’s easy to think of digital media and experiences – including our new AI explorations – as ethereal things that magically traverse the computing cloud to enlighten and entertain us. Gerry McGovern is here to remind you that that’s far from the case, that “digital is physical.” The data centers that power cloud computing are lapping up water and consuming electricity at an alarming pace, and the arrival of AI is accelerating these troubling patterns of overconsumption.
Interview transcript
Larry:
Hi, everyone. Welcome to episode number 23 of the Content + AI podcast. I am really delighted today to welcome to the show Gerry McGovern. Gerry is the author of the book The World Wide Waste: How Digital is Killing the Planet and What to Do About It. He’s also probably better known … and I originally met him almost 15, 20 years ago when he was talking about customer care words, and subsequently out of that arose, I think, his work on top task methodology. So anyhow, Gerry’s a well-established figure in the discipline, has a lot of important stuff to tell us about the environmental costs of AI. But welcome, Gerry. Tell the folks a little bit more about what you’re up to these days.
Gerry:
Thank you, Larry. It’s lovely to be speaking to you again. I suppose what I’m up to mainly is … In a sense, I never thought it would happen, but I’ve become a type of environmental activist focused on the impacts of digital and how to use digital in a better way, in a less damaging way. I don’t think digital can be green in any sense, but I think it can be used to help more our environment and at least to reduce the damage it causes to our environment. So, that’s the main stuff I’m focused on.
Larry:
Yeah. Well, I got to say, I love the idea that you’re an environmental activist now, because we need plenty of that. But one of the things about your work that I think has really driven home the point to me that we think of digital as this ephemeral thing happening out there in the ether. It’s like no consequence. You can just throw stuff in a hard drive or share something. But this is still connected to the physical world, right?
Gerry:
Absolutely. And the first sentence in The World Wide Waste says, “Digital is physical,” and basically, the cloud … It’s on the ground in these mega data centers that are … They say between now and ’27, data centers will add the equivalent electricity demand of a Germany, or perhaps a Japan, of electricity demand to the global electricity network. So it’s growing at a phenomenal pace, the quantity of architecture that’s out there. It’s very, very much physical.
Larry:
That’s just amazing. And one of the things that the people building those giant server farms and things they’re good at is, you don’t really hear that much about it. They’re almost doing reverse PR or something.
Gerry:
Oh, yeah. It’s one of the most secretive, least transparent industries on Earth, and deliberately so. It’s all part of the plan. They will never reply to a press call, or very, very rarely. They’ve become a little bit more in the last, but it’s secrecy, secrecy, secrecy, secrecy, doubled on secrecy, secrecy, secrecy, secrecy. When they buy, you don’t even know who owns the data center until the very last minute in the process. So it’s all super, super, super, super secrecy stuff, because they know they don’t have a good story to tell to the local community or the local area, because data centers are absolutely horrible for a local community. There’s little or no jobs, a couple of security jobs. There might be 20 people, maybe 40 people maximum in a mega, mega data center running it, so they bring little or nothing to the local community. They might bring some tax, but behind the scenes, they’re often getting more in tax breaks than what they’re bringing. So there’s not a good story to tell, and therefore they try and stay as secretive as possible.
Larry:
Interesting. And one of the things you were talking about, that I’m reminded that these are often in small communities out in the boondocks, because a key driver in these things is the need for water to cool. And can you talk a little bit about that, the types of communities that are affected by this, and that thing that you said, that the local governments are giving in tax breaks but getting almost nothing back?
Gerry:
Yeah. Certainly a large data center, which is a lot, mainly these big ones, these super data centers … There are these massive, big warehouses, and they can be quite nice as well, so you don’t want them close to homes. You don’t want them very close to homes, and they need a huge electrical infrastructure, so you need utilities and backup. A lot of them have these mega backup diesel generators so that they’ve all sorts of redundancies. And then they’ve a massive water demand, hundreds and hundreds of thousands of liters, of gallons of water a day. And with AI, that’s going to grow maybe five or 10, because with artificial intelligence, it’s much more processing-driven, and the more processing there is, the more heat there is in the environment. The more heat there is, the more need for cooling. So Microsoft’s water demand, I think, went up 20% in a year in 2022.
Gerry:
So we’re talking about mega water demands, and ironically, still, you find them in places like Phoenix or whatever, which is strangely … Phoenix, Arizona in the United States, which is undergoing a hundred-year drought, which is essentially close to a desert. But water is really cheap, or certainly historically has been really cheap in the process, because they’ve got this massive underground aquifer that has built up over millions of years and that they’re essentially draining dry. It’s not just the data centers. It’s the industrial farming. And now the chip manufacturers, who are incredibly water-intensive as well, are coming there for political reasons, because of the US-China conflicts. So, you’ve got a lot of incredible material intensity behind the scenes of this stuff. I saw one study that said that by 2030, an average European would be using as much water for their digital activities as they drink on a daily basis.
Larry:
Wow.
Gerry:
And that’s just the water.
Larry:
I have to tell you, I lived in Phoenix. Before I moved to Europe, I was living in Phoenix, Arizona, and on a flight back to Phoenix from someplace … I can’t remember where … I was seated next to a guy who worked for one of those big chip manufacturers, and I said, “What are you doing in Arizona? There’s no water here.” And he goes, “Oh, there’s plenty of water.” So like you just said, the chip manufacturers think that, and those are unreplaceable aquifers. Is there data about … For example, you can probably compute when Phoenix, Arizona will run out of water, or any number of other places in the world. Are there people looking at that?
Gerry:
There are. I think in the US, it’s not the only place, but something like 80% of US aquifers are stressed, so watersheds are stressed. Arizona has a weird plan at the moment. They’re looking to send a pipe down to the … I don’t know if it’s the Gulf of Mexico, the ocean in Mexico … and pipe water from the ocean, which is going to be very expensive, because it’s much more expensive to desalinate water than it is to use fresh water. Because these data centers, they need very clean water for all sorts of reasons. You can’t get dust or pollutants in the pipes and et cetera in the process.
Gerry:
So there are some plans there, but generally speaking, there was a study there recently in the New York Times that says the East Coast of the US is saggy because they’ve extracted so much water. And I think if you would’ve driven around Arizona a bit, you would’ve found quite a bit of collapsed land, because when these aquifers empty and the ground subsides and collapse, they’ll never fill again, even if it rains, because there’s no space for them to actually fill, or at least certainly take them a million years to fill.
Gerry:
So, yes and no. The scientists are saying yes, but basically people are pumping water like there’s no tomorrow, and basically that chip manufacturer will move on to Costa Rica or Puerto Rico. These organizations, they’re nominally from the United States, but really they go wherever. So they’ll drain Arizona and then they’ll be gone, because they’ve no loyalty to Arizona. They’ve no loyalty, they don’t intend to live there, none of those executives. They’ll be gone to the next place where they can get cheap water. That’s the nature of the behavior, unfortunately, that has got us into this environmental crisis.
Larry:
You’re reminding me, as you say that, of the secrecy that surrounds this. And one of the things that’s interesting to me about that is that it’s completely counter to one of the main design principles when you’re designing AI products or AI services, of transparency and explainability and trustworthiness and all that. Is that just a random irony, or is this like it’s poison down to the bottom or something like that?
Gerry:
Oh, I think so. There is no transparency in AI. I’ve been following AI since the eighties. I had an interest in science fiction and stuff like that, and many of the early designers of AI deliberately wanted it to be opaque, and they were almost excited by this idea of unknowability. So, AI design is designed for non-transparency. The people who design AI do not know how it gets its answers. They have some vague idea, but they’re constantly tweaking it, hoping it’ll do the right thing. But they’ve essentially designed a system where you cannot track back, so if you’re a citizen and you’ve been denied benefits, you can’t really go back to the core logic of why that happened. You can get some surface logic, but because it’s designed layers upon layers upon layers upon layers upon layers, and when you add these massive quantities of content … and of course the low quality. AI has been designed on the web. That should worry people.
Gerry:
The web is, in general … and having us all worked in content … It’s not even like McDonald’s. It’s like the dumpster outside McDonald’s. That’s what the web is from a content perspective. It’s like you didn’t even bring AI to the cheapest restaurant in town. You brought AI to the dumpster behind the cheapest restaurant in town, and you fed it. And now you hear stories that they’re going to get it from Reddit as well, the community, and there’s some good stuff there. But yes, so there’s a hell of a lot of junk, probably 90% useless junk that AI has been fed on over the last five or 10 years, and all sorts of horrible, violent stuff.
Gerry:
And I know we’re jumping a little bit, but there’s a big industry … There’s this magic, that the AI people are kind of these 18th century magicians who would … snake oil salesmen who go from town to town. There’s a hell of a lot of manual labor behind AI to train their systems. They usually send them to places in Kenya or in Venezuela or in Bangladesh, where hundreds, thousands, thousands of people are tweaking and cleaning out the violent content, et cetera, doing, of course, incredible damage to their mental health, being paid a dollar an hour or even less. So now they’re targeting Argentina, because the Argentinian economy has collapsed. So all these things like Mechanical Turk and Hive and stuff like that basically get thousands and thousands of freelance people to clean up the crap and to tweak it, and what you have in some AI systems is actually humans behind the scenes pretending to be AI in the process.
Gerry:
So, there’s a lot of magic going on here. It’s not nearly as good as a snake oil sales rep positioning is, and it’s prone to make incredible mistakes, and many believe there’s no way to fix this because they really don’t know what’s happening in the process. So they’ve designed this thing that they really don’t control, and you hear people like Sam prepping for the apocalypse, and the guru and god of AI saying that he’s prepping. He’s bought this big area of land, and his bunkers, and his water, and his masks and all. So here’s the guy who is trying to sell us all this stuff, who’s got his escape route along with Zuckerberg, et cetera, if and when they totally blow up the planet because of their greed and negligence, basically.
Larry:
Yeah. That’s just horrible, and the fact that they have to … They spend half their time worrying about how they’re going to keep their security guards from killing them after the apocalypse. So anyhow, that’s beyond my comprehension, but as you’re talking about this, I’m reminded that a lot of the environmental costs, they’re urgent and we got to deal with them, but the social costs are manifesting right away right in our face, like these Kenyan workers who are traumatized by having to do this, and then more in addition to the secrecy due to the lack of transparency about how much actual human activity is still behind a lot of these things.
Larry:
Have you had any success stories? Because one thing I want to observe is, on LinkedIn lately, you’re sharing a hundred things a day. It’s great. It’s like this lovely fire hose of information about this stuff. But I’m wondering if you’ve seen any success stories in pulling back the veil. You’re doing a great job of that, so maybe you’re it, but have you seen other success stories of folks pulling back the veil and kind of showing the real social, environmental, and other costs of AI?
Gerry:
Oh, there are a number of researchers. There’s stuff happening at the University of Oxford. There’s a wonderful person … I’m probably not going to say her name properly … Timnit Gebru. She was actually fired by Google. She is a major AI researcher and visionary, and three or four years ago, she was fired by Google because she brought up these concerns. Her and some other, interestingly, women are at the forefront of raising the awareness about, whereas men are just, “Oh, let’s go forward. Progress, progress, progress at any cost, at any consequence,” and that’s the culture. It’s an accelerationist culture. But there are quite a few researchers in the academic world that are raising the red flags about that. So yeah, there’s a paper I just got. I think the name of the woman is Ana Valdivia. She’s done stuff on AI and the impacts in Latin America in particular.
Gerry:
And if we go back a step, everything is very dependent on chips, and AI is dependent on a particular type of chip, which is a very heavy processing type of chip, but these chip manufacturing environments are … To manufacture a chip is 400 chemicals, and so different chemicals involved, and some of those chemicals … I talked to a chemical expert, and he says at best, we understand less than 1% of chemicals and their impact on the environment, because in industry there’s just tens of thousands of chemicals. But one of the chemical types that people are becoming really aware of the damage is called PFAS, which are otherwise known as forever chemicals, and they’re called forever chemicals because essentially, they do not break down in the environment. And they’re responsible emerging for all sorts of … a whole panoply of diseases and human conditions. They’re not good stuff at all.
Gerry:
Now, the semiconductor industry has stated quite clearly that these are essential for semiconductor manufacturing, so you can’t make chips without forever chemicals. So essentially they’re saying, “Yeah, that …” A study came out recently said, in the water everywhere … whether it’s in the Antarctic, whether it’s in the middle of the ocean, whether it’s in Ireland, whether it’s in Amsterdam, whether it’s in Spain … wherever rain falls, there are forever chemicals now. So they’ve so spread, and the impacts, and the semiconductor industry is saying that we can’t make chips without forever chemicals, so we’re just going to have to keep using them. That’s just one area of chip manufacturing. A typical chip will probably consume about 40 liters of water, so it’s not just … The data centers are incredibly intense, and will become more intense, because these AI chips are so hot because they do so much processing.
Gerry:
So, there’s two types of heating and cooling that can occur. One is more air conditioning, so it’s taking electricity, which often is coming from a dam which is connected with water, but at least it’s not consuming as much water because it’s air-based. But the cheaper way, historically, has been water-based, so that’s why they went to Arizona and places like that, because cheap water. Water’s about half the cost of air conditioning, water-based cooling systems. Now, if you’ve got very, very hot chips, you can’t cool them with air. They’re just too hot to cool with air because they’re also intensely packed together, these AI chips, so you have to cool them with water. So AI chips and AI data centers are going to be twice, three, four times more water-intensive. And we have a global fresh water crisis, and they want fresh water as well. They don’t want all dodgy waste waters. They need the best of water in the process.
Gerry:
So the material impact of all of this, because an AI … If you ask a question of Google, say, a standard question, and you ask the same question of ChatGPT … same basic question, same basic answer … ChatGPT is probably 10 times more energetic in getting its answer than the flat Google search engine. So we’re developing these systems that … for convenience, or because they’re cool, or because they give us sentences and paragraphs, because they give great audio, because they can do video … that are much more energy-intense than pulling an index, pulling 10 pieces of metadata from a flat type of index. So for our convenience, and to churn out 600 blog posts in an hour … most of them just don’t make any sense … that’s very energetic behind the scenes.
Gerry:
Now people say, “Well, it’s still less energetic than a person writing 600 blog posts in an hour.” Well, a person can’t write 600 blog posts in an hour. So yes, if you compare our blog post, it is more efficient to do it with AI. But if you are churning out 600 or a thousand, the overall cost is still greater and you haven’t really benefited any, because it’s just this content circling the drain, race to the bottom, this content arms race, that now I have to do 10,000 an hour to beat the search engines. Oh, now I have to do 20,000 an hour to beat the search engines. And then what’s also happening is the AI content is feeding on the AI content, and this is … In the area, I think they call it the doom loop, because this is really bad for the AI systems when they start training on their … It’s like they’re eating their own poo in the process. So, there’s so much that’s coming down the line with all of this stuff that is just not good.
Larry:
Yeah, I’ve heard that phenomenon you last described as model collapse. I think that’s how the AI people talk about it. But I want to go back to … As you’re talking about this, has anybody attempted to quantify the … I remember Denny Vrandecic from Wikipedia did a talk at the Knowledge Graph Conference last year and pointed out the same thing that you did, that to get the same answer from Google versus an LLM chat agent is about 10x. So there’s that, and that’s like … If you’re a business like Google, if your day-to-day, transactional costs multiply by 10, that’s a lot right there, but that’s just the tip of the iceberg. You’re talking about the environmental costs of everything from the water that needs to cool these things to the extraction of rare earth materials to build these things, and then the forever chemicals. Has anybody attempted to add this all up?
Gerry:
Well, some have, Larry, but see, the thing that’s going to pay for this is us buying more stuff, because the meta crisis that is destroying life on earth, destroying our environment, is over consumption. Obviously, population is a factor. If there was less people on the planet, there would be less stress. But rich people, essentially, middle-class people … The top 10% are doing 50% of environmental damage. The bottom 50% are doing 10%, and the top 10% are doing 50% because of what they consume, because of two-ton SUVs, because of constant weekend breaks, flying, et cetera, et cetera. So, it’s the consumption patterns. We consume too much, and what we consume very quickly turns into waste, fast fashion or whatever it is. Nothing lasts.
Gerry:
Now, who is developing AI? Who are the core developers? It’s Google, it’s Facebook, it’s Microsoft. But really Microsoft Bing in the market, a lot of activity. Amazon as well. So, what are these entities? Well, they’re advertising entities. These are advertising agencies. Google is an advertising agency. Facebook is an advertising agency. Amazon is an advertising agency and a discounter, a cheap discounter. In essence, that’s how Amazon grew. It’s a cheap discounter. Their whole business model is getting us to consume more. So AI will be judged by these companies based on, can they increase our consumption? That’s how they’ve got to pay for their money. You’re exactly right. It’s 10x more. It’s much more architecture. It’s more expensive chips, electrical infrastructure, pipe. There’s a lot of impacts out of this. So, the way we’re going to pay for it is through consuming more.
Gerry:
So if you look at all the impacts of AI from an infrastructure point of view, it’s like the tip of the melting iceberg. The hidden part of the iceberg is the over consumption that AI needs to drive to pay for itself. So, AI is basically an environmental doom loop. It can only be successful by getting us to massively increase consumption, and it is consumption that is destroying the soil. It is consumption that’s destroying our water. Soil degradation, 30, 40% soil degradation. We lose our soil, we won’t be able to grow anything anymore. Soil is desertification, et cetera. So we’ve got novel entities, we’ve got plastic. There’ll be more plastic in the ocean in a few years than, basically, fish. Mining to build all these things. We’ll soon be creating a Mount Everest of mining waste every year, a Mount Everest of mining waste. And no, no environment on the planet can survive getting a Mount Everest dug up every single year to build all the infrastructure, our cars and our computers and all those other stuff.
Gerry:
So if we don’t slow down radically really, really, really quickly, it’s very hard to see how the species will survive another 50 or a hundred years in the process. And AI is a maximizer of acceleration, so AI is the exact opposite thing of slowing down. It’s about acceleration. And these tech bros either think they can survive in a bunker or that they’ll go to Mars or something and be able to wave goodbye to the rest of us as they head off to Mars, but they don’t realize that Mars is a hundred times worse than Ireland could ever be even in the worst case scenario. You’ve got dust storms there going at 600 kilometers an hour, 10,000 kilometers an hour or whatever in the process. So you wonder, where is the wisdom in all this? We’ve got all these super-intelligent people, but they seem to have no real deep sense of wisdom of what they’re actually doing.
Larry:
Yeah, and this is exactly why I wanted to have you on, Gerry, because you set this out so clearly and so well. And it goes all the way up and down, from the natural planet to the society to individual people, and the fact that consumption is at the root of this and that this whole shooting match accelerates consumption, that’s not good. Anyhow, Gerry, I could literally talk to you about this for days, but I like to keep these episodes a manageable length. But is there anything last, anything you really want to share before we wrap up?
Gerry:
Well, I think summarizing all that, slow down. Somebody asked me, “What can we do in relation?” So the brain consumes about 20 watts an hour, I think, or something like that. The brain is incredibly efficient, but if you’re not doing any thinking, if you’re not really using your brain, you’re still consuming that 20 watts. And that 20 watts, cost about a kg of CO2 a day, somewhere in that territory. So we have an engine in our selves, we’ve an engine intelligence, et cetera, but it’s also a polluting system as well. It also causes an impact on the climate. And the basic thing would be, I would say, is slow down and use our brain more, use our body more in the process, because this energy and waste is occurring anyway, and at least most of it is natural, which is a good thing.
Gerry:
So if you’ve got an issue, try and remember it, and if you’re finding you don’t remember, do practice your memory. Don’t lose your memory. So start with trying to remember stuff, because I often would go to Google because it would be easier to go to Google than to remember a telephone number, or an appointment, or an address, or how to get there in the process. So, come back to ourselves and particularly our kids. Focus on their memory, memory games, et cetera in the process. The next thing is, keep it local with everything, growing your food, et cetera in the process. But also, if you’re storing stuff, store it local, store it on a hard drive, store it on a disk. It’s much more energy efficient if you manage it properly. But also, if you can’t remember stuff, look in your notes, look in a physical book, look in something you’ve got on the hard drive, not in the cloud. So try and find it there as the next step. You can’t find it in your memory, try and find it locally in the process.
Gerry:
And then if you have to go to the web, go to a search engine first and foremost, because remember, 10 times more energy efficient. So, try and create this scale of energy. It’s the same in text. A text message is thousands of times more energy efficient than a video. The least polluting energy impact way to send communication is to text, mobile texting in the process, so text is hundreds of times less impactful than images. Images are hundreds of times less than videos. So there’s a scale of energetic use and waste, and let’s look at our energy consumption, our waste production. And we can do a lot, but slowing down as a big thing, because when we slow down as well, we make wiser decisions. The faster we call, the worse our decision-making processes become. Radically slow down. Slow down everything, consumption, et cetera in the process, and be careful how we choose. Start with our memory. Keep it local. Only as a last resort go to the highly energetic artificial environment.
Larry:
I really appreciate that, Gerry, I think especially a nice actionable list of things that folks can do at the end. Hey, one very last thing. If folks want to follow you online or connect, what’s the best place to find you online?
Gerry:
Yeah, so I left X or Twitter years ago, so I’m on Mastodon and LinkedIn as well. So they’re the two areas, and GerryMcGovern.com as well. I’m publishing stuff on that, and the weekly newsletter that I started in 1996. I’m still going.
Larry:
Nice. I appreciate your ongoing contributions, Gerry. Well, thank you so much. Really great to talk again.
Gerry:
Thank you, Larry. Thank you for having me on.
