Elizabeth Holmes Through the Director’s Lens
Kira Peikoff was the editor-in-chief of Leaps.org from 2017 to 2021. As a journalist, her work has appeared in The New York Times, Newsweek, Nautilus, Popular Mechanics, The New York Academy of Sciences, and other outlets. She is also the author of four suspense novels that explore controversial issues arising from scientific innovation: Living Proof, No Time to Die, Die Again Tomorrow, and Mother Knows Best. Peikoff holds a B.A. in Journalism from New York University and an M.S. in Bioethics from Columbia University. She lives in New Jersey with her husband and two young sons. Follow her on Twitter @KiraPeikoff.
"The Inventor," a chronicle of Theranos's storied downfall, premiered recently on HBO. Leapsmag reached out to director Alex Gibney, whom The New York Times has called "one of America's most successful and prolific documentary filmmakers," for his perspective on Elizabeth Holmes and the world she inhabited.
Do you think Elizabeth Holmes was a charismatic sociopath from the start — or is she someone who had good intentions, over-promised, and began the lies to keep her business afloat, a "fake it till you make it" entrepreneur like Thomas Edison?
I'm not qualified to say if EH was or is a sociopath. I don't think she started Theranos as a scam whose only purpose was to make money. If she had done so, she surely would have taken more money for herself along the way. I do think that she had good intentions and that she, as you say, "began the lies to keep her business afloat." ([Reporter John] Carreyrou's book points out that those lies began early.) I think that the Edison comparison is instructive for a lot of reasons.
First, Edison was the original "fake-it-till-you-make-it" entrepreneur. That puts this kind of behavior in the mainstream of American business. By saying that, I am NOT endorsing the ethic, just the opposite. As one Enron executive mused about the mendacity there, "Was it fraud or was it bad marketing?" That gives you a sense of how baked-in the "fake it" sensibility is.
"Having a thirst for fame and a noble cause enabled her to think it was OK to lie in service of those goals."
I think EH shares one other thing with Edison, which is a huge ego coupled with a talent for storytelling as long as she is the heroic, larger-than-life main character. It's interesting that EH calls her initial device "Edison." Edison was the world's most famous "inventor," both because of the devices that came out of his shop and and for his ability for "self-invention." As Randall Stross notes in "The Wizard of Menlo Park," he was the first celebrity businessman. In addition to her "good intentions," EH was certainly motivated by fame and glory and many of her lies were in service to those goals.
Having a thirst for fame and a noble cause enabled her to think it was OK to lie in service of those goals. That doesn't excuse the lies. But those noble goals may have allowed EH to excuse them for herself or, more perniciously, to make believe that they weren't lies at all. This is where we get into scary psychological territory.
But rather than thinking of it as freakish, I think it's more productive to think of it as an exaggeration of the way we all lie to others and to ourselves. That's the point of including the Dan Ariely experiment with the dice. In that experiment, most of the subjects cheated more when they thought they were doing it for a good cause. Even more disturbing, that "good cause" allowed them to lie much more effectively because they had come to believe they weren't doing anything wrong. As it turns out, economics isn't a rational practice; it's the practice of rationalizing.
Where EH and Edison differ is that Edison had a firm grip on reality. He knew he could find a way to make the incandescent lightbulb work. There is no evidence that EH was close to making her "Edison" work. But rather than face reality (and possibly adjust her goals) she pretended that her dream was real. That kind of "over-promising" or "bold vision" is one thing when you are making a prototype in the lab. It's a far more serious matter when you are using a deeply flawed system on real patients. EH can tell herself that she had to do that (Walgreens was ready to walk away if she hadn't "gone live") or else Theranos would have run out of money.
But look at the calculation she made: she thought it was worth putting lives at risk in order to make her dream come true. Now we're getting into the realm of the sociopath. But my experience leads me to believe that -- as in the case of the Milgram experiment -- most people don't do terrible things right away, they come to crimes gradually as they become more comfortable with bigger and bigger rationalizations. At Theranos, the more valuable the company became, the bigger grew the lies.
The two whistleblowers come across as courageous heroes, going up against the powerful and intimidating company. The contrast between their youth and lack of power and the old elite backers of Theronos is staggering, and yet justice triumphed. Were the whistleblowers hesitant or afraid to appear in the film, or were they eager to share their stories?
By the time I got to them, they were willing and eager to tell their stories, once I convinced them that I would honor their testimony. In the case of Erika and Tyler, they were nudged to participate by John Carreyrou, in whom they had enormous trust.
"It's simply crazy that no one demanded to see an objective demonstration of the magic box."
Why do you think so many elite veterans of politics and venture capitalism succumbed to Holmes' narrative in the first place, without checking into the details of its technology or financials?
The reasons are all in the film. First, Channing Robertson and many of the old men on her board were clearly charmed by her and maybe attracted to her. They may have rationalized their attraction by convincing themselves it was for a good cause! Second, as Dan Ariely tells us, we all respond to stories -- more than graphs and data -- because they stir us emotionally. EH was a great storyteller. Third, the story of her as a female inventor and entrepreneur in male-dominated Silicon Valley is a tale that they wanted to invest in.
There may have been other factors. EH was very clever about the way she put together an ensemble of credibility. How could Channing Robertson, George Shultz, Henry Kissinger and Jim Mattis all be wrong? And when Walgreens put the Wellness Centers in stores, investors like Rupert Murdoch assumed that Walgreens must have done its due diligence. But they hadn't!
It's simply crazy that no one demanded to see an objective demonstration of the magic box. But that blind faith, as it turns out, is more a part of capitalism than we have been taught.
Do you think that Roger Parloff deserves any blame for the glowing Fortune story on Theranos, since he appears in the film to blame himself? Or was he just one more victim of Theranos's fraud?
He put her on the cover of Fortune so he deserves some blame for the fraud. He still blames himself. That willingness to hold himself to account shows how seriously he takes the job of a journalist. Unlike Elizabeth, Roger has the honesty and moral integrity to admit that he made a mistake. He owned up to it and published a mea culpa. That said, Roger was also a victim because Elizabeth lied to him.
Do you think investors in Silicon Valley, with their FOMO attitudes and deep pockets, are vulnerable to making the same mistake again with a shiny new startup, or has this saga been a sober reminder to do their due diligence first?
Many of the mistakes made with Theranos were the same mistakes made with Enron. We must learn to recognize that we are, by nature, trusting souls. Knowing that should lead us to a guiding slogan: "trust but verify."
The irony of Holmes dancing to "I Can't Touch This" is almost too perfect. How did you find that footage?
It was leaked to us.
"Elizabeth Holmes is now famous for her fraud. Who better to host the re-boot of 'The Apprentice.'"
Holmes is facing up to 20 years in prison for federal fraud charges, but Vanity Fair recently reported that she is seeking redemption, taking meetings with filmmakers for a possible documentary to share her "real" story. What do you think will become of Holmes in the long run?
It's usually a mistake to handicap a trial. My guess is that she will be convicted and do some prison time. But maybe she can convince jurors -- the way she convinced journalists, her board, and her investors -- that, on account of her noble intentions, she deserves to be found not guilty. "Somewhere, over the rainbow…"
After the trial, and possibly prison, I'm sure that EH will use her supporters (like Tim Draper) to find a way to use the virtual currency of her celebrity to rebrand herself and launch something new. Fitzgerald famously said that "there are no second acts in American lives." That may be the stupidest thing he ever said.
Donald Trump failed at virtually every business he ever embarked on. But he became a celebrity for being a fake businessman and used that celebrity -- and phony expertise -- to become president of the United States. Elizabeth Holmes is now famous for her fraud. Who better to host the re-boot of "The Apprentice." And then?
"You Can't Touch This!"
Kira Peikoff was the editor-in-chief of Leaps.org from 2017 to 2021. As a journalist, her work has appeared in The New York Times, Newsweek, Nautilus, Popular Mechanics, The New York Academy of Sciences, and other outlets. She is also the author of four suspense novels that explore controversial issues arising from scientific innovation: Living Proof, No Time to Die, Die Again Tomorrow, and Mother Knows Best. Peikoff holds a B.A. in Journalism from New York University and an M.S. in Bioethics from Columbia University. She lives in New Jersey with her husband and two young sons. Follow her on Twitter @KiraPeikoff.
Researchers probe extreme gene therapy for severe alcoholism
Story by Freethink
A single shot — a gene therapy injected into the brain — dramatically reduced alcohol consumption in monkeys that previously drank heavily. If the therapy is safe and effective in people, it might one day be a permanent treatment for alcoholism for people with no other options.
The challenge: Alcohol use disorder (AUD) means a person has trouble controlling their alcohol consumption, even when it is negatively affecting their life, job, or health.
In the U.S., more than 10 percent of people over the age of 12 are estimated to have AUD, and while medications, counseling, or sheer willpower can help some stop drinking, staying sober can be a huge struggle — an estimated 40-60 percent of people relapse at least once.
A team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
According to the CDC, more than 140,000 Americans are dying each year from alcohol-related causes, and the rate of deaths has been rising for years, especially during the pandemic.
The idea: For occasional drinkers, alcohol causes the brain to release more dopamine, a chemical that makes you feel good. Chronic alcohol use, however, causes the brain to produce, and process, less dopamine, and this persistent dopamine deficit has been linked to alcohol relapse.
There is currently no way to reverse the changes in the brain brought about by AUD, but a team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
To find out, they tested it in heavy-drinking monkeys — and the animals’ alcohol consumption dropped by 90% over the course of a year.
How it works: The treatment centers on the protein GDNF (“glial cell line-derived neurotrophic factor”), which supports the survival of certain neurons, including ones linked to dopamine.
For the new study, a harmless virus was used to deliver the gene that codes for GDNF into the brains of four monkeys that, when they had the option, drank heavily — the amount of ethanol-infused water they consumed would be equivalent to a person having nine drinks per day.
“We targeted the cell bodies that produce dopamine with this gene to increase dopamine synthesis, thereby replenishing or restoring what chronic drinking has taken away,” said co-lead researcher Kathleen Grant.
To serve as controls, another four heavy-drinking monkeys underwent the same procedure, but with a saline solution delivered instead of the gene therapy.
The results: All of the monkeys had their access to alcohol removed for two months following the surgery. When it was then reintroduced for four weeks, the heavy drinkers consumed 50 percent less compared to the control group.
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
The researchers then took the alcohol away for another four weeks, before giving it back for four. They repeated this cycle for a year, and by the end of it, the treated monkeys’ consumption had fallen by more than 90 percent compared to the controls.
“Drinking went down to almost zero,” said Grant. “For months on end, these animals would choose to drink water and just avoid drinking alcohol altogether. They decreased their drinking to the point that it was so low we didn’t record a blood-alcohol level.”
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
Looking ahead: Dopamine is involved in a lot more than addiction, so more research is needed to not only see if the results translate to people but whether the gene therapy leads to any unwanted changes to mood or behavior.
Because the therapy requires invasive brain surgery and is likely irreversible, it’s unlikely to ever become a common treatment for alcoholism — but it could one day be the only thing standing between people with severe AUD and death.
“[The treatment] would be most appropriate for people who have already shown that all our normal therapeutic approaches do not work for them,” said Grant. “They are likely to create severe harm or kill themselves or others due to their drinking.”
This article originally appeared on Freethink, home of the brightest minds and biggest ideas of all time.
Massive benefits of AI come with environmental and human costs. Can AI itself be part of the solution?
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.