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.
Could epigenetic reprogramming reverse aging?
A range of strategies are being explored to reprogram the body's cells to an earlier state. Most scientists aren't betting on a new fountain of youth just yet - but, in theory, epigenetic reprogramming is a recipe for self-renewal.
Ten thousand years ago, the average human spent a maximum of 30 years on Earth. Despite the glory of Ancient Greece and the Roman Empire, most of their inhabitants didn’t surpass the age of 35. Between the 1500s and 1800, life expectancy (at least in Europe) fluctuated between 30 and 40 years.
Public health advancements like control of infectious diseases, better diet and clean sanitation, as well as social improvements have made it possible for human lifespans to double since 1800. Although lifespan differs widely today from country to country according to socioeconomic health, the average has soared to 73.2 years.
But this may turn out to be on the low side if epigenetic rejuvenation fulfills its great promise: to reverse aging, perhaps even completely. Epigenetic rejuvenation, or partial reprogramming, is the process by which a set of therapies are trying to manipulate epigenetics – how various changes can affect our genes – and the Yamanaka factors. These Yamanaka factors are a group of proteins that can convert any cell of the body into pluripotent stem cells, a group of cells that can turn into brand new cells, such as those of the brain or skin. At least in theory, it could be a recipe for self-renewal.
“Partial reprogramming tries to knock a few years off of people’s biological age, while preserving their original cell identity and function,” says Yuri Deigin, cofounder and director of YouthBio Therapeutics, a longevity startup utilizing partial reprogramming to develop gene therapies aimed at the renewal of epigenetic profiles. YouthBio plans to experiment with injecting these gene therapies into target organs. Once the cargo is delivered, a specific small molecule will trigger gene expression and rejuvenate those organs.
“Our ultimate mission is to find the minimal number of tissues we would need to target to achieve significant systemic rejuvenation,” Deigin says. Initially, YouthBio will apply these therapies to treat age-related conditions. Down the road, though, their goal is for everyone to get younger. “We want to use them for prophylaxis, which is rejuvenation that would lower disease risk,” Deigin says.
Epigenetics has swept the realm of biology off its feet over the last decade. We now know that we can switch genes on and off by tweaking the chemical status quo of the DNA’s local environment. "Epigenetics is a fascinating and important phenomenon in biology,’’ says Henry Greely, a bioethicist at Stanford Law School. Greely is quick to stress that this kind of modulation (turning genes on and off and not the entire DNA) happens all the time. “When you eat and your blood sugar goes up, the gene in the beta cells of your pancreas that makes insulin is turned on or up. Almost all medications are going to have effects on epigenetics, but so will things like exercise, food, and sunshine.”
Can intentional control over epigenetic mechanisms lead to novel and useful therapies? “It is a very plausible scenario,” Greely says, though a great deal of basic research into epigenetics is required before it becomes a well-trodden way to stay healthy or treat disease. Whether these therapies could cause older cells to become younger in ways that have observable effects is “far from clear,” he says. “Historically, betting on someone’s new ‘fountain of youth’ has been a losing strategy.”
The road to de-differentiation, the process by which cells return to an earlier state, is not paved with roses; de-differentiate too much and you may cause pathology and even death.
In 2003 researchers finished sequencing the roughly 3 billion letters of DNA that make up the human genome. The human genome sequencing was hailed as a vast step ahead in our understanding of how genetics contribute to diseases like cancer or to developmental disorders. But for Josephine Johnston, director of research and research scholar at the Hastings Center, the hype has not lived up to its initial promise. “Other than some quite effective tests to diagnose certain genetic conditions, there isn't a radical intervention that reverses things yet,” Johnston says. For her, this is a testament to the complexity of biology or at least to our tendency to keep underestimating it. And when it comes to epigenetics specifically, Johnston believes there are some hard questions we need to answer before we can safely administer relevant therapies to the population.
“You'd need to do longitudinal studies. You can't do a study and look at someone and say they’re safe only six months later,” Johnston says. You can’t know long-term side effects this way, and how will companies position their therapies on the market? Are we talking about interventions that target health problems, or life enhancements? “If you describe something as a medical intervention, it is more likely to be socially acceptable, to attract funding from governments and ensure medical insurance, and to become a legitimate part of medicine,” she says.
Johnston’s greatest concerns are of the philosophical and ethical nature. If we’re able to use epigenetic reprogramming to double the human lifespan, how much of the planet’s resources will we take up during this long journey? She believes we have a moral obligation to make room for future generations. “We should also be honest about who's actually going to afford such interventions; they would be extraordinarily expensive and only available to certain people, and those are the people who would get to live longer, healthier lives, and the rest of us wouldn't.”
That said, Johnston agrees there is a place for epigenetic reprogramming. It could help people with diseases that are caused by epigenetic problems such as Fragile X syndrome, Prader-Willi syndrome and various cancers.
Zinaida Good, a postdoctoral fellow at Stanford Cancer Institute, says these problems are still far in the future. Any change will be incremental. “Thinking realistically, there’s not going to be a very large increase in lifespan anytime soon,” she says. “I would not expect something completely drastic to be invented in the next 5 to 10 years. ”
Good won’t get any such treatment for herself until it’s shown to be effective and safe. Nature has programmed our bodies to resist hacking, she says, in ways that could undermine any initial benefits to longevity. A preprint that is not yet peer-reviewed reports cellular reprogramming may lead to premature death due to liver and intestinal problems, and using the Yamanaka factors may have the potential to cause cancer, at least in animal studies.
“Side effects are an open research question that all partial reprogramming companies and labs are trying to address,” says Deigin. The road to de-differentiation, the process by which cells return to an earlier state, is not paved with roses; de-differentiate too much and you may cause pathology and even death. Deigin is exploring other, less risky approaches. “One way is to look for novel factors tailored toward rejuvenation rather than de-differentiation.” Unlike Yamanaka factors, such novel factors would never involve taking a given cell to a state in which it could turn cancerous, according to Deigin.
An example of a novel factor that could lower the risk of cancer is artificially introducing mRNA molecules, or molecules carrying the genetic information necessary to make proteins, by using electricity to penetrate the cell instead of a virus. There is also chemical-based reprogramming, in which chemicals are applied to convert regular cells into pluripotent cells. This approach is currently effective only for mice though.
“The search for novel factors tailored toward rejuvenation without de-differentiation is an ongoing research and development effort by several longevity companies, including ours,” says Deigin.
He isn't disclosing the details of his own company’s underlying approach to lowering the risk, but he’s hopeful that something will eventually end up working in humans. Yet another challenge is that, partly because of the uncertainties, the FDA hasn’t seen fit to approve a single longevity therapy. But with the longevity market projected to soar to $600 billion by 2025, Deigin says naysayers are clinging irrationally to the status quo. “Thankfully, scientific progress is moved forward by those who bet for something while disregarding the skeptics - who, in the end, are usually proven wrong.”
Podcast: The Friday Five Weekly Roundup in Health Research
In this week's Friday Five, a new face mask can detect Covid and send an alert to your phone. Plus, promising research for a breakthrough drug to treat schizophrenia, an AI tool that can create new proteins, progress on a longevity drug - and more.
The Friday Five covers five stories in research that you may have missed this week. There are plenty of controversies and troubling ethical issues in science – and we get into many of them in our online magazine – but this news roundup focuses on scientific creativity and progress to give you a therapeutic dose of inspiration headed into the weekend.
Here are the promising studies covered in this week's Friday Five:
- A new mask can detect Covid and send an alert to your phone
- More promising research for a breakthrough drug to treat schizophrenia
- AI tool can create new proteins
- Connections between an unhealthy gut and breast cancer
- Progress on the longevity drug, rapamycin
And an honorable mention this week: Certain exercises may benefit some types of memory more than others