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.
Researchers at Stanford have found that the virus that causes Covid-19 can infect fat cells, which could help explain why obesity is linked to worse outcomes for those who catch Covid-19.
Obesity is a risk factor for worse outcomes for a variety of medical conditions ranging from cancer to Covid-19. Most experts attribute it simply to underlying low-grade inflammation and added weight that make breathing more difficult.
Now researchers have found a more direct reason: SARS-CoV-2, the virus that causes Covid-19, can infect adipocytes, more commonly known as fat cells, and macrophages, immune cells that are part of the broader matrix of cells that support fat tissue. Stanford University researchers Catherine Blish and Tracey McLaughlin are senior authors of the study.
Most of us think of fat as the spare tire that can accumulate around the middle as we age, but fat also is present closer to most internal organs. McLaughlin's research has focused on epicardial fat, “which sits right on top of the heart with no physical barrier at all,” she says. So if that fat got infected and inflamed, it might directly affect the heart.” That could help explain cardiovascular problems associated with Covid-19 infections.
Looking at tissue taken from autopsy, there was evidence of SARS-CoV-2 virus inside the fat cells as well as surrounding inflammation. In fat cells and immune cells harvested from health humans, infection in the laboratory drove "an inflammatory response, particularly in the macrophages…They secreted proteins that are typically seen in a cytokine storm” where the immune response runs amok with potential life-threatening consequences. This suggests to McLaughlin “that there could be a regional and even a systemic inflammatory response following infection in fat.”
It is easy to see how the airborne SARS-CoV-2 virus infects the nose and lungs, but how does it get into fat tissue? That is a mystery and the source of ample speculation.
The macrophages studied by McLaughlin and Blish were spewing out inflammatory proteins, While the the virus within them was replicating, the new viral particles were not able to replicate within those cells. It was a different story in the fat cells. “When [the virus] gets into the fat cells, it not only replicates, it's a productive infection, which means the resulting viral particles can infect another cell,” including microphages, McLaughlin explains. It seems to be a symbiotic tango of the virus between the two cell types that keeps the cycle going.
It is easy to see how the airborne SARS-CoV-2 virus infects the nose and lungs, but how does it get into fat tissue? That is a mystery and the source of ample speculation.
Macrophages are mobile; they engulf and carry invading pathogens to lymphoid tissue in the lymph nodes, tonsils and elsewhere in the body to alert T cells of the immune system to the pathogen. Perhaps some of them also carry the virus through the bloodstream to more distant tissue.
ACE2 receptors are the means by which SARS-CoV-2 latches on to and enters most cells. They are not thought to be common on fat cells, so initially most researchers thought it unlikely they would become infected.
However, while some cell receptors always sit on the surface of the cell, other receptors are expressed on the surface only under certain conditions. Philipp Scherer, a professor of internal medicine and director of the Touchstone Diabetes Center at the University of Texas Southwestern Medical Center, suggests that, in people who have obesity, “There might be higher levels of dysfunctional [fat cells] that facilitate entry of the virus,” either through transiently expressed ACE2 or other receptors. Inflammatory proteins generated by macrophages might contribute to this process.
Another hypothesis is that viral RNA might be smuggled into fat cells as cargo in small bits of material called extracellular vesicles, or EVs, that can travel between cells. Other researchers have shown that when EVs express ACE2 receptors, they can act as decoys for SARS-CoV-2, where the virus binds to them rather than a cell. These scientists are working to create drugs that mimic this decoy effect as an approach to therapy.
Do fat cells play a role in Long Covid? “Fat cells are a great place to hide. You have all the energy you need and fat cells turn over very slowly; they have a half-life of ten years,” says Scherer. Observational studies suggest that acute Covid-19 can trigger the onset of diabetes especially in people who are overweight, and that patients taking medicines to regulate their diabetes “were actually quite protective” against acute Covid-19. Scherer has funding to study the risks and benefits of those drugs in animal models of Long Covid.
McLaughlin says there are two areas of potential concern with fat tissue and Long Covid. One is that this tissue might serve as a “big reservoir where the virus continues to replicate and is sent out” to other parts of the body. The second is that inflammation due to infected fat cells and macrophages can result in fibrosis or scar tissue forming around organs, inhibiting their function. Once scar tissue forms, the tissue damage becomes more difficult to repair.
Current Covid-19 treatments work by stopping the virus from entering cells through the ACE2 receptor, so they likely would have no effect on virus that uses a different mechanism. That means another approach will have to be developed to complement the treatments we already have. So the best advice McLaughlin can offer today is to keep current on vaccinations and boosters and lose weight to reduce the risk associated with obesity.
Air pollution can lead to lung cancer. The connection suggests new ways to stop cancer in its tracks.
Researchers at Francis Crick Institute found that air pollution can "wake up" existing mutations, triggering them to turn into lung cancer. The scientists used their new understanding about this connection to prevent cancer in mice.
Forget taking a deep breath. Around the world, 99 percent of people breathe air polluted to unsafe levels, according to data from the World Health Organization. Activities such as burning fossil fuels release greenhouse gases that contribute to air pollution, which could lead to heart disease, stroke, asthma, emphysema, and some types of cancer.
“The burden of disease attributable to air pollution is now estimated to be on a par with other major global health risks such as unhealthy diet and tobacco smoking, and air pollution is now recognized as the single biggest environmental threat to human health,” wrote the authors of a 2021 WHO report.
The majority of lung cancer is attributed to smoking. But as pollution levels have increased, and anti-smoking campaigns have discouraged smoking, the proportion of lung cancers diagnosed in non-smokers has grown. The CDC estimates that 10 to 20 percent of lung cancers in the U.S. currently occur in non-smokers.
The mechanism between air pollution and the development of lung cancer has been unclear, but researchers at London’s Francis Crick Institute recently made an important breakthrough in understanding the connection. Lead investigator Charles Swanton presented this research last month at a conference in Paris.
Pollution awakens mutations
The Crick Institute scientists were able to identify a new link between common air pollutants and non-small cell lung cancer (NSCLC). They focused on pollutants called particulate matter, or PM, that are 2.5 microns wide, narrower than human cells.
Most cancer diagnosed in non-smoking people is NSCLC, but this type of cancer hasn’t received the same research attention as more common lung cancers found in smokers, according to Clare Weeden, a cancer researcher at the Crick Institute and a co-author of the study.
“This is a really underserved and under-researched population that we really need to tackle, as well as lung cancers that occur in smokers,” she says. “Lung cancer is the number one cancer killer worldwide.”
In the past, some researchers believed air pollution caused mutations that led to cancer. Others believed these mutations could remain dormant without any detriment to health until pollutants or other stressors triggered them to become cancerous. Reviving the latter hypothesis that carcinogens may activate pre-existing mutations, instead of directly causing them, the Crick researchers analyzed samples from 463,679 people in the UK and parts of Asia, noting mutations and comparing changes in gene expression in mice and human cells.
“The mutation can exist in a nascent clone without causing cancer,” says Emilia Lim, a bioinformatics expert and a co-first author of the Crick study. “It is the carcinogen that promotes a conducive environment for this one little clone to grow and expand into cancer. Through our work, we were able to revive excitement for this hypothesis and bring it to light.”
The study explains a confusing pattern of lung cancer developing, particularly in women, despite a lack of environmental risk factors like smoking, secondhand smoke, or radon exposure. The culprit in these cases may have been too much PM 2.5 exposure.
Other researchers had previously identified a link between mutations in certain genes that control epidermal growth factor receptors, or EGFR mutations, and the development of NSCLC. In a 2019 study of 250 people with this type of cancer, about 32 percent had the mutation. Women are more likely to have EGFR mutations than men.
Not everyone who has the EGFR mutation will develop lung cancer. Respirologist Stephen Lam studies lung cancer at the BC Cancer Research Centre in Vancouver, Canada, but was not involved in the Crick Institute research. He says the study explains a confusing pattern of lung cancer developing, particularly in women, despite a lack of environmental risk factors like smoking, secondhand smoke, or radon exposure. The culprit in these cases may have been too much PM 2.5 exposure.
More exposure leads to inflammation and lesions
The Crick researchers found that an excess of PM 2.5 in the air sparked an inflammatory process in cells within the lung. This inflammation set the stage for NSCLC to develop in people and mice with existing EGFR mutations.
The researchers also exposed mice without EGFR mutations to PM 2.5 pollution—an experiment that couldn’t be ethically conducted in humans—to link pollution exposure to NSCLC. The mice experiments also showed that NSCLC is dose-dependent; higher levels of exposure were associated with higher number of cancerous lesions forming.
Ultimately, the study “fundamentally changed how we view lung cancer in people who have never smoked,” said Swanton in a Crick Institute press release. “Cells with cancer-causing mutations accumulate naturally as we age, but they are normally inactive. We’ve demonstrated that air pollution wakes these cells up in the lungs, encouraging them to grow and potentially form tumors.”
Preventing cancer before it begins
Targeted therapies already exist for people with EGFR mutations who’ve developed NSCLC, but they have many side effects, according to Weeden. Researchers hope that making more definitive links between pollutants and cancer could help prevent people with EGFR or other mutations from developing lung cancer in the first place.
Along those lines, as an additional component of their study presented last month, the Crick researchers were able to prevent cancer in mice that had the EGFR mutations by blocking inflammation. They used an antibody to inhibit a protein called interleukin 1 beta, which plays a key role in inflammation. Scientists could eventually use such antibodies or other therapies to make a drug treatment that people can take to stop cancer in its tracks, even if they live in highly polluted areas.
Such potential could reach beyond lung cancer; in the past, Crick and other researchers have also found associations between exposure to air pollution and mesothelioma, as well as cancers of the small intestine, lip, mouth, and throat. These links could be meaningful to a growing number of people as climate change intensifies, and with increases in air pollution from fossil fuel combustion and natural disasters like forest fires.
Plus, air pollution is just one external condition that can flip the switch of these inflammatory pathways. Identifying a link between pollution and cancer “has wide ramifications for many other environmental factors that may [play] similar roles,” Weedon says. She hopes that the Crick study and future research in this area will offer some hope for non-smokers frustrated by cancer diagnoses.