Massive benefits of AI come with environmental and human costs. Can AI itself be part of the solution?
Generative AI has a large carbon footprint and other drawbacks. But AI can help mitigate its own harms—by plowing through mountains of data on extreme weather and human displacement.
The recent explosion of generative artificial intelligence tools like ChatGPT and Dall-E enabled anyone with internet access to harness AI’s power for enhanced productivity, creativity, and problem-solving. With their ever-improving capabilities and expanding user base, these tools proved useful across disciplines, from the creative to the scientific.
But beneath the technological wonders of human-like conversation and creative expression lies a dirty secret—an alarming environmental and human cost. AI has an immense carbon footprint. Systems like ChatGPT take months to train in high-powered data centers, which demand huge amounts of electricity, much of which is still generated with fossil fuels, as well as water for cooling. “One of the reasons why Open AI needs investments [to the tune of] $10 billion from Microsoft is because they need to pay for all of that computation,” says Kentaro Toyama, a computer scientist at the University of Michigan. There’s also an ecological toll from mining rare minerals required for hardware and infrastructure. This environmental exploitation pollutes land, triggers natural disasters and causes large-scale human displacement. Finally, for data labeling needed to train and correct AI algorithms, the Big Data industry employs cheap and exploitative labor, often from the Global South.
Generative AI tools are based on large language models (LLMs), with most well-known being various versions of GPT. LLMs can perform natural language processing, including translating, summarizing and answering questions. They use artificial neural networks, called deep learning or machine learning. Inspired by the human brain, neural networks are made of millions of artificial neurons. “The basic principles of neural networks were known even in the 1950s and 1960s,” Toyama says, “but it’s only now, with the tremendous amount of compute power that we have, as well as huge amounts of data, that it’s become possible to train generative AI models.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries.
In recent months, much attention has gone to the transformative benefits of these technologies. But it’s important to consider that these remarkable advances may come at a price.
AI’s carbon footprint
In their latest annual report, 2023 Landscape: Confronting Tech Power, the AI Now Institute, an independent policy research entity focusing on the concentration of power in the tech industry, says: “The constant push for scale in artificial intelligence has led Big Tech firms to develop hugely energy-intensive computational models that optimize for ‘accuracy’—through increasingly large datasets and computationally intensive model training—over more efficient and sustainable alternatives.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries. In 2019, Emma Strubell, then a graduate researcher at the University of Massachusetts Amherst, estimated that training a single LLM resulted in over 280,000 kg in CO2 emissions—an equivalent of driving almost 1.2 million km in a gas-powered car. A couple of years later, David Patterson, a computer scientist from the University of California Berkeley, and colleagues, estimated GPT-3’s carbon footprint at over 550,000 kg of CO2 In 2022, the tech company Hugging Face, estimated the carbon footprint of its own language model, BLOOM, as 25,000 kg in CO2 emissions. (BLOOM’s footprint is lower because Hugging Face uses renewable energy, but it doubled when other life-cycle processes like hardware manufacturing and use were added.)
Luckily, despite the growing size and numbers of data centers, their increasing energy demands and emissions have not kept pace proportionately—thanks to renewable energy sources and energy-efficient hardware.
But emissions don’t tell the full story.
AI’s hidden human cost
“If historical colonialism annexed territories, their resources, and the bodies that worked on them, data colonialism’s power grab is both simpler and deeper: the capture and control of human life itself through appropriating the data that can be extracted from it for profit.” So write Nick Couldry and Ulises Mejias, authors of the book The Costs of Connection.
The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
Technologies we use daily inexorably gather our data. “Human experience, potentially every layer and aspect of it, is becoming the target of profitable extraction,” Couldry and Meijas say. This feeds data capitalism, the economic model built on the extraction and commodification of data. While we are being dispossessed of our data, Big Tech commodifies it for their own benefit. This results in consolidation of power structures that reinforce existing race, gender, class and other inequalities.
“The political economy around tech and tech companies, and the development in advances in AI contribute to massive displacement and pollution, and significantly changes the built environment,” says technologist and activist Yeshi Milner, who founded Data For Black Lives (D4BL) to create measurable change in Black people’s lives using data. The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
AI’s recent explosive growth spiked the demand for manual, behind-the-scenes tasks, creating an industry described by Mary Gray and Siddharth Suri as “ghost work” in their book. This invisible human workforce that lies behind the “magic” of AI, is overworked and underpaid, and very often based in the Global South. For example, workers in Kenya who made less than $2 an hour, were the behind the mechanism that trained ChatGPT to properly talk about violence, hate speech and sexual abuse. And, according to an article in Analytics India Magazine, in some cases these workers may not have been paid at all, a case for wage theft. An exposé by the Washington Post describes “digital sweatshops” in the Philippines, where thousands of workers experience low wages, delays in payment, and wage theft by Remotasks, a platform owned by Scale AI, a $7 billion dollar American startup. Rights groups and labor researchers have flagged Scale AI as one company that flouts basic labor standards for workers abroad.
It is possible to draw a parallel with chattel slavery—the most significant economic event that continues to shape the modern world—to see the business structures that allow for the massive exploitation of people, Milner says. Back then, people got chocolate, sugar, cotton; today, they get generative AI tools. “What’s invisible through distance—because [tech companies] also control what we see—is the massive exploitation,” Milner says.
“At Data for Black Lives, we are less concerned with whether AI will become human…[W]e’re more concerned with the growing power of AI to decide who’s human and who’s not,” Milner says. As a decision-making force, AI becomes a “justifying factor for policies, practices, rules that not just reinforce, but are currently turning the clock back generations years on people’s civil and human rights.”
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement.
Nuria Oliver, a computer scientist, and co-founder and vice-president of the European Laboratory of Learning and Intelligent Systems (ELLIS), says that instead of focusing on the hypothetical existential risks of today’s AI, we should talk about its real, tangible risks.
“Because AI is a transverse discipline that you can apply to any field [from education, journalism, medicine, to transportation and energy], it has a transformative power…and an exponential impact,” she says.
AI's accountability
“At the core of what we were arguing about data capitalism [is] a call to action to abolish Big Data,” says Milner. “Not to abolish data itself, but the power structures that concentrate [its] power in the hands of very few actors.”
A comprehensive AI Act currently negotiated in the European Parliament aims to rein Big Tech in. It plans to introduce a rating of AI tools based on the harms caused to humans, while being as technology-neutral as possible. That sets standards for safe, transparent, traceable, non-discriminatory, and environmentally friendly AI systems, overseen by people, not automation. The regulations also ask for transparency in the content used to train generative AIs, particularly with copyrighted data, and also disclosing that the content is AI-generated. “This European regulation is setting the example for other regions and countries in the world,” Oliver says. But, she adds, such transparencies are hard to achieve.
Google, for example, recently updated its privacy policy to say that anything on the public internet will be used as training data. “Obviously, technology companies have to respond to their economic interests, so their decisions are not necessarily going to be the best for society and for the environment,” Oliver says. “And that’s why we need strong research institutions and civil society institutions to push for actions.” ELLIS also advocates for data centers to be built in locations where the energy can be produced sustainably.
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement. “The only way to make sense of this data is using machine learning methods,” Oliver says.
Milner believes that the best way to expose AI-caused systemic inequalities is through people's stories. “In these last five years, so much of our work [at D4BL] has been creating new datasets, new data tools, bringing the data to life. To show the harms but also to continue to reclaim it as a tool for social change and for political change.” This change, she adds, will depend on whose hands it is in.
A company uses AI to fight muscle loss and unhealthy aging
In December 2022, a company called BioAge Labs published findings on a drug that worked to prevent muscular atrophy, or the loss of muscle strength and mass, in older people.
There’s a growing need to slow down the aging process. The world’s population is getting older and, according to one estimate, 80 million Americans will be 65 or older by 2040. As we age, the risk of many chronic diseases goes up, from cancer to heart disease to Alzheimer’s.
BioAge Labs, a company based in California, is using genetic data to help people stay healthy for longer. CEO Kristen Fortney was inspired by the genetics of people who live long lives and resist many age-related diseases. In 2015, she started BioAge to study them and develop drug therapies based on the company’s learnings.
The team works with special biobanks that have been collecting blood samples and health data from individuals for up to 45 years. Using artificial intelligence, BioAge is able to find the distinctive molecular features that distinguish those who have healthy longevity from those who don’t.
In December 2022, BioAge published findings on a drug that worked to prevent muscular atrophy, or the loss of muscle strength and mass, in older people. Much of the research on aging has been in worms and mice, but BioAge is focused on human data, Fortney says. “This boosts our chances of developing drugs that will be safe and effective in human patients.”
How it works
With assistance from AI, BioAge measures more than 100,000 molecules in each blood sample, looking at proteins, RNA and metabolites, or small molecules that are produced through chemical processes. The company uses many techniques to identify these molecules, some of which convert the molecules into charged atoms and then separating them according to their weight and charge. The resulting data is very complex, with many thousands of data points from patients being followed over the decades.
BioAge validates its targets by examining whether a pathway going awry is actually linked to the development of diseases, based on the company’s analysis of biobank health records and blood samples. The team uses AI and machine learning to identify these pathways, and the key proteins in the unhealthy pathways become their main drug targets. “The approach taken by BioAge is an excellent example of how we can harness the power of big data and advances in AI technology to identify new drugs and therapeutic targets,” says Lorna Harries, a professor of molecular genetics at the University of Exeter Medical School.
Martin Borch Jensen is the founder of Gordian Biotechnology, a company focused on using gene therapy to treat aging. He says BioAge’s use of AI allows them to speed up the process of finding promising drug candidates. However, it remains a challenge to separate pathologies from aspects of the natural aging process that aren’t necessarily bad. “Some of the changes are likely protective responses to things going wrong,” Jensen says. “Their data doesn’t…distinguish that so they’ll need to validate and be clever.”
Developing a drug for muscle loss
BioAge decided to focus on muscular atrophy because it affects many elderly people, making it difficult to perform everyday activities and increasing the risk of falls. Using the biobank samples, the team modeled different pathways that looked like they could improve muscle health. They found that people who had faster walking speeds, better grip strength and lived longer had higher levels of a protein called apelin.
Apelin is a peptide, or a small protein, that circulates in the blood. It is involved in the process by which exercise increases and preserves muscle mass. BioAge wondered if they could prevent muscular atrophy by increasing the amount of signaling in the apelin pathway. Instead of the long process of designing a drug, they decided to repurpose an existing drug made by another biotech company. This company, called Amgen, had explored the drug as a way to treat heart failure. It didn’t end up working for that purpose, but BioAge took note that the drug did seem to activate the apelin pathway.
BioAge tested its new, repurposed drug, BGE-105, and, in a phase 1 clinical trial, it protected subjects from getting muscular atrophy compared to a placebo group that didn’t receive the drug. Healthy volunteers over age 65 received infusions of the drug during 10 days spent in bed, as if they were on bed rest while recovering from an illness or injury; the elderly are especially vulnerable to muscle loss in this situation. The 11 people taking BGE-105 showed a 100 percent improvement in thigh circumference compared to 10 people taking the placebo. Ultrasound observations also revealed that the group taking the durg had enhanced muscle quality and a 73 percent increase in muscle thickness. One volunteer taking BGE-105 did have muscle loss compared to the the placebo group.
Heather Whitson, the director of the Duke University Centre for the study of aging and human development, says that, overall, the results are encouraging. “The clinical findings so far support the premise that AI can help us sort through enormous amounts of data and identify the most promising points for beneficial interventions.”
More studies are needed to find out which patients benefit the most and whether there are side effects. “I think further studies will answer more questions,” Whitson says, noting that BGE-105 was designed to enhance only one aspect of physiology associated with exercise, muscle strength. But exercise itself has many other benefits on mood, sleep, bones and glucose metabolism. “We don’t know whether BGE-105 will impact these other outcomes,” she says.
The future
BioAge is planning phase 2 trials for muscular atrophy in patients with obesity and those who have been hospitalized in an intensive care unit. Using the data from biobanks, they’ve also developed another drug, BGE-100, to treat chronic inflammation in the brain, a condition that can worsen with age and contributes to neurodegenerative diseases. The team is currently testing the drug in animals to assess its effects and find the right dose.
BioAge envisions that its drugs will have broader implications for health than treating any one specific disease. “Ultimately, we hope to pioneer a paradigm shift in healthcare, from treatment to prevention, by targeting the root causes of aging itself,” Fortney says. “We foresee a future where healthy longevity is within reach for all.”
How old fishing nets turn into chairs, car mats and Prada bags
A company called Aquafil is turning the fibers from fishing nets and old carpets into new threads for chairs, car mats, bikinis and Prada bags.
Discarded nylon fishing nets in the oceans are among the most harmful forms of plastic pollution. Every year, about 640,000 tons of fishing gear are left in our oceans and other water bodies to turn into death traps for marine life. London-based non-profit World Animal Protection estimates that entanglement in this “ghost gear” kills at least 136,000 seals, sea lions and large whales every year. Experts are challenged to estimate how many birds, turtles, fish and other species meet the same fate because the numbers are so high.
Since 2009, Giulio Bonazzi, the son of a small textile producer in northern Italy, has been working on a solution: an efficient recycling process for nylon. As CEO and chairman of a company called Aquafil, Bonazzi is turning the fibers from fishing nets – and old carpets – into new threads for car mats, Adidas bikinis, environmentally friendly carpets and Prada bags.
For Bonazzi, shifting to recycled nylon was a question of survival for the family business. His parents founded a textile company in 1959 in a garage in Verona, Italy. Fifteen years later, they started Aquafil to produce nylon for making raincoats, an enterprise that led to factories on three continents. But before the turn of the century, cheap products from Asia flooded the market and destroyed Europe’s textile production. When Bonazzi had finished his business studies and prepared to take over the family company, he wondered how he could produce nylon, which is usually produced from petrochemicals, in a way that was both successful and ecologically sustainable.
The question led him on an intellectual journey as he read influential books by activists such as world-renowned marine biologist Sylvia Earle and got to know Michael Braungart, who helped develop the Cradle-to-Cradle ethos of a circular economy. But the challenges of applying these ideologies to his family business were steep. Although fishing nets have become a mainstay of environmental fashion ads—and giants like Dupont and BASF have made breakthroughs in recycling nylon—no one had been able to scale up these efforts.
For ten years, Bonazzi tinkered with ideas for a proprietary recycling process. “It’s incredibly difficult because these products are not made to be recycled,” Bonazzi says. One complication is the variety of materials used in older carpets. “They are made to be beautiful, to last, to be useful. We vastly underestimated the difficulty when we started.”
Soon it became clear to Bonazzi that he needed to change the entire production process. He found a way to disintegrate old fibers with heat and pull new strings from the discarded fishing nets and carpets. In 2022, his company Aquafil produced more than 45,000 tons of Econyl, which is 100% recycled nylon, from discarded waste.
More than half of Aquafil’s recyclate is from used goods. According to the company, the recycling saves 90 percent of the CO2 emissions compared to the production of conventional nylon. That amounts to saving 57,100 tons of CO2 equivalents for every 10,000 tons of Econyl produced.
Bonazzi collects fishing nets from all over the world, including Norway and Chile—which have the world’s largest salmon productions—in addition to the Mediterranean, Turkey, India, Japan, Thailand, the Philippines, Pakistan, and New Zealand. He counts the government leadership of Seychelles as his most recent client; the island has prohibited ships from throwing away their fishing nets, creating the demand for a reliable recycler. With nearly 3,000 employees, Aquafil operates almost 40 collection and production sites in a dozen countries, including four collection sites for old carpets in the U.S., located in California and Arizona.
First, the dirty nets are gathered, washed and dried. Bonazzi explains that nets often have been treated with antifouling agents such as copper oxide. “We recycle the coating separately,” he says via Zoom from his home near Verona. “Copper oxide is a useful substance, why throw it away?”
Still, only a small percentage of Aquafil’s products are made from nets fished out of the ocean, so your new bikini may not have saved a strangled baby dolphin. “Generally, nylon recycling is a good idea,” says Christian Schiller, the CEO of Cirplus, the largest global marketplace for recyclates and plastic waste. “But contrary to what consumers think, people rarely go out to the ocean to collect ghost nets. Most are old, discarded nets collected on land. There’s nothing wrong with this, but I find it a tad misleading to label the final products as made from ‘ocean plastic,’ prompting consumers to think they’re helping to clean the oceans by buying these products.”
Aquafil gets most of its nets from aqua farms. Surprisingly, one of Aquafil’s biggest problems is finding enough waste. “I know, it’s hard to believe because waste is everywhere,” Bonazzi says. “But we need to find it in reliable quantity and quality.” He has invested millions in establishing reliable logistics to source the fishing nets. Then the nets get shredded into granules that can be turned into car mats for the new Hyundai Ioniq 5 or a Gucci swimsuit.
The process works similarly with carpets. In the U.S. alone, 3.5 billion pounds of carpet are discarded in landfills every year, and less than 3 percent are currently recycled. Aquafil has built a recycling plant in Phoenix to help divert 12,500 tons of carpets from the landfill every year. The carpets are shredded and deconstructed into three components: fillers such as calcium carbonate will be reused in the cement industry, synthetic fibers like polypropylene can be used for engineering plastics, and nylon. Only the pelletized nylon gets shipped back to Europe for the production of Econyl. “We ship only what’s necessary,” Bonazzi says. Nearly 50 percent of his nylon in Italy and Slovenia is produced from recyclate, and he hopes to increase the percentage to two-thirds in the next two years.
His clients include Interface, the leading world pioneer for sustainable flooring, and many other carpet producers plus more than 2500 fashion labels, including Gucci, Prada, Patagonia, Louis Vuitton, Adidas and Stella McCartney. “Stella McCartney just introduced a parka that’s made 100 percent from Econyl,” Bonazzi says. “We’re also in a lot of sportswear because Nylon is a good fabric for swimwear and for yoga clothes.” Next, he’s looking into sunglasses and chairs made with Econyl - for instance, the flexible ergonomic noho chair, designed by New Zealand company Formway.
“When I look at a landfill, I see a gold mine," Bonazzi says.
“Bonazzi decided many years ago to invest in the production of recycled nylon though industry giants halted similar plans after losing large investments,” says Anika Herrmann, vice president of the German Greentech-competitor Camm Solutions, which creates bio-based polymers from cane sugar and other ag waste. “We need role models like Bonazzi who create sustainable solutions with courage and a pioneering spirit. Like Aquafil, we count on strategic partnerships to enable fast upscaling along the entire production chain.”
Bonazzi’s recycled nylon is still five to 10 percent more expensive than conventionally produced material. However, brands are increasingly bending to the pressure of eco-conscious consumers who demand sustainable fashion. What helped Bonazzi was the recent rise of oil prices and the pressure on industries to reduce their carbon footprint. Now Bonazzi says, “When I look at a landfill, I see a gold mine.”
Ideally, the manufacturers take the products back when the client is done with it, and because the nylon can theoretically be reused nearly infinitely, the chair or bikini could be made into another chair or bikini. “But honestly,” Bonazzi half-jokes, “if someone returns a McCartney parka to me, I’ll just resell it because it’s so expensive.”
The next step: Bonazzi wants to reshape the entire nylon industry by pivoting from post-consumer nylon to plant-based nylon. In 2017, he began producing “nylon-6,” together with Genomatica in San Diego. The process uses sugar instead of petroleum. “The idea is to make the very same molecule from sugar, not from oil,” he says. The demonstration plant in Ljubljana, Slovenia, has already produced several hundred tons of nylon, and Genomatica is collaborating with Lululemon to produce plant-based yoga wear.
Bonazzi acknowledges that his company needs a few more years before the technology is ready to meet his ultimate goal, producing only recyclable products with no petrochemicals, low emissions and zero waste on an industrial scale. “Recycling is not enough,” he says. “You also need to produce the primary material in a sustainable way, with a low carbon footprint.”