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.
Here's how one doctor overcame extraordinary odds to help create the birth control pill
Dr. Percy Julian had so many personal and professional obstacles throughout his life, it’s amazing he was able to accomplish anything at all. But this hidden figure not only overcame these incredible obstacles, he also laid the foundation for the creation of the birth control pill.
Julian’s first obstacle was growing up in the Jim Crow-era south in the early part of the twentieth century, where racial segregation kept many African-Americans out of schools, libraries, parks, restaurants, and more. Despite limited opportunities and education, Julian was accepted to DePauw University in Indiana, where he majored in chemistry. But in college, Julian encountered another obstacle: he wasn’t allowed to stay in DePauw’s student housing because of segregation. Julian found lodging in an off-campus boarding house that refused to serve him meals. To pay for his room, board, and food, Julian waited tables and fired furnaces while he studied chemistry full-time. Incredibly, he graduated in 1920 as valedictorian of his class.
After graduation, Julian landed a fellowship at Harvard University to study chemistry—but here, Julian ran into yet another obstacle. Harvard thought that white students would resent being taught by Julian, an African-American man, so they withdrew his teaching assistantship. Julian instead decided to complete his PhD at the University of Vienna in Austria. When he did, he became one of the first African Americans to ever receive a PhD in chemistry.
Julian received offers for professorships, fellowships, and jobs throughout the 1930s, due to his impressive qualifications—but these offers were almost always revoked when schools or potential employers found out Julian was black. In one instance, Julian was offered a job at the Institute of Paper Chemistory in Appleton, Wisconsin—but Appleton, like many cities in the United States at the time, was known as a “sundown town,” which meant that black people weren’t allowed to be there after dark. As a result, Julian lost the job.
During this time, Julian became an expert at synthesis, which is the process of turning one substance into another through a series of planned chemical reactions. Julian synthesized a plant compound called physostigmine, which would later become a treatment for an eye disease called glaucoma.
In 1936, Julian was finally able to land—and keep—a job at Glidden, and there he found a way to extract soybean protein. This was used to produce a fire-retardant foam used in fire extinguishers to smother oil and gasoline fires aboard ships and aircraft carriers, and it ended up saving the lives of thousands of soldiers during World War II.
At Glidden, Julian found a way to synthesize human sex hormones such as progesterone, estrogen, and testosterone, from plants. This was a hugely profitable discovery for his company—but it also meant that clinicians now had huge quantities of these hormones, making hormone therapy cheaper and easier to come by. His work also laid the foundation for the creation of hormonal birth control: Without the ability to synthesize these hormones, hormonal birth control would not exist.
Julian left Glidden in the 1950s and formed his own company, called Julian Laboratories, outside of Chicago, where he manufactured steroids and conducted his own research. The company turned profitable within a year, but even so Julian’s obstacles weren’t over. In 1950 and 1951, Julian’s home was firebombed and attacked with dynamite, with his family inside. Julian often had to sit out on the front porch of his home with a shotgun to protect his family from violence.
But despite years of racism and violence, Julian’s story has a happy ending. Julian’s family was eventually welcomed into the neighborhood and protected from future attacks (Julian’s daughter lives there to this day). Julian then became one of the country’s first black millionaires when he sold his company in the 1960s.
When Julian passed away at the age of 76, he had more than 130 chemical patents to his name and left behind a body of work that benefits people to this day.
Therapies for Healthy Aging with Dr. Alexandra Bause
My guest today is Dr. Alexandra Bause, a biologist who has dedicated her career to advancing health, medicine and healthier human lifespans. Dr. Bause co-founded a company called Apollo Health Ventures in 2017. Currently a venture partner at Apollo, she's immersed in the discoveries underway in Apollo’s Venture Lab while the company focuses on assembling a team of investors to support progress. Dr. Bause and Apollo Health Ventures say that biotech is at “an inflection point” and is set to become a driver of important change and economic value.
Previously, Dr. Bause worked at the Boston Consulting Group in its healthcare practice specializing in biopharma strategy, among other priorities
She did her PhD studies at Harvard Medical School focusing on molecular mechanisms that contribute to cellular aging, and she’s also a trained pharmacist
In the episode, we talk about the present and future of therapeutics that could increase people’s spans of health, the benefits of certain lifestyle practice, the best use of electronic wearables for these purposes, and much more.
Dr. Bause is at the forefront of developing interventions that target the aging process with the aim of ensuring that all of us can have healthier, more productive lifespans.
