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.
Should You Bank Your Kid’s Teeth for Stem Cells?
When Karen Davis attended a presentation at a dental conference in 2013, she unexpectedly discovered a service that could help her daughter, Madeline: storing stem cells derived from her teeth that potentially could be used in the future to treat her Crohn's disease.
"Even though this isn't a viable option today, I know how rapidly things can change."
Throughout high school, Madeline suffered from the painful autoimmune disorder, which wreaks havoc on the gastrointestinal system and can lead to life-threatening complications.
"I leave no stone unturned when it comes to medical care and this resonated with me," says Davis, a Dallas-based dental hygienist who was encouraged by advances in stem cell research. Later that year, when Madeline got her wisdom teeth extracted, Davis shipped them off to the Store-A-Tooth company in Massachusetts, where they will be kept frozen until needed. "Even though this isn't a viable option today, I know how rapidly things can change," says Davis. "To me, this was a worthwhile investment—I didn't want to miss out on an opportunity that would provide a pathway to a cure."
Karen Davis pictured with her daughter Madeline.
(Courtesy of Karen Davis)
The process itself was straightforward. Madeline's newly extracted wisdom teeth--baby teeth can be saved, too—were bathed in a special solution, loaded into a Styrofoam container lined with cold packs and sent to the stem cell company. There, a team harvested the dental stem cells from the pulp, then grew them in culture and cryogenically preserved them. Store-A-Tooth charges $1500-1749 for tooth collection and $120 per year for storage, while other dental pulp stem cell tissue banks cost $500-$600 upfront and in the $120 range annually for storage.
The rationale here is that if you missed out on banking your baby's umbilical cord blood, this gives you another chance to harvest their stem cells. "If their child later develops an illness that could be managed or even cured with stem cell therapy, this is an insurance policy," says Amr Moursi, DDS, PhD, chair of the department of pediatric dentistry at New York University College of Dentistry.
But is there a genuine potential here for some effective treatments in the relatively near future—or is this just another trendy fad? Scientific opinion is decidedly mixed. Stem cells have been heralded as the next frontier in medicine because of their versatility: with a little chemical coaxing, they can be transformed into different cell types, such as heart, blood or brain cells, to create tissue that can mend damaged body parts. Because they're taken from your own body, there's little chance of rejection, which means patients don't have to take strong antirejection drugs that can have all sorts of unpleasant side effects for the rest of their lives.
However, while stem cells are immature cells found in different tissues, ranging from abdominal fat to bone marrow, there is a vast difference between the stem cells found in cord blood and in teeth. Cord blood, which is culled from the umbilical cord when a baby is born, contains what are called hematopoietic stem cells (HSCs), which can mature into other blood cells. These type of stem cells have already been approved by the U.S. Food and Drug Administration to treat patients—especially children--with blood cancers, such as leukemias and lymphomas, and certain blood disorders like sickle cell anemia.
In contrast, stem cells in teeth are called mesenchymal stem cells (MSCs), which are found in dental pulp, the tissue in the center of the tooth that's filled with nerves and blood vessels. MSCs are adult stem cells normally found in the bone marrow that can transform into bone, fat, and cartilage cells, and also aid in the formation of blood stem cells.
"Right now we just don't have rigorous evidence that they can be used in that fashion and have real benefit."
Small studies on lab animals suggest that MSCs secrete growth factors—hormonal steroids or proteins—that can nurture ailing cells, act as powerful anti-inflammatory agents that could tame autoimmune disorders like the one that plagues Karen Davis's daughter, and may even generate new nerve and muscle tissue. Preliminary research suggests they potentially could treat medical conditions as varied as heart disease, spinal cord injury and type 1 diabetes by generating new cells, which can replace damaged or dead cells.
But this is all very early research and there's a vast difference between how cells behave in the tightly controlled environment of a lab versus the real world in a diverse population of human patients. "Right now we just don't have rigorous evidence that they can be used in that fashion and have real benefit," says Pamela G. Robey, PhD, chief of the skeletal biology section at the National Institute of Dental and Craniofacial Research at the National Institutes of Health.
Robey should know—she headed the research team that discovered stem cells in human baby teeth and in wisdom teeth more than fifteen years ago. She believes prospects are better using these stem cells for tooth repair: research suggests they may be able to fix cracked teeth, repair bone defects caused by gum disease, or in root canal therapy, where they can be used to replace infected tissue with regenerated healthy pulp.
In the meantime, though, there are no clinical applications for MSCs. "These tooth banking companies aren't doing their own research," says Leigh Turner, a bioethicist at the University of Minnesota who monitors stem cell clinics. "They cobble together reports of early research in humans or from animal studies in an effort to provide a narrative to make it seem like it is evidence based."
Still, in all fairness, tooth banking companies aren't making the kind of extravagant claims made by stem cell clinics, which operate in a gray area of the law and purport to treat everything from chronic lung disease to Alzheimer's. "We don't know when therapies will be available using these cells because the pace of research is hard to predict," says Peter Verlander, PhD, a molecular geneticist and chief scientific officer of Provia Laboratories, the parent company of Store-A-Tooth. "But for parents who regretted not banking their child's cord blood, especially if they later develop a disease like diabetes, this is another opportunity."
But the jury is still out if this is truly a good investment. Moursi, a national spokesperson for the American Academy of Pediatric Dentistry who fields queries about this practice from a dozen or so families a year, concludes: "If you could afford it, and know the risks, benefits and current limitations, then it is something to consider."
The Death Predictor: A Helpful New Tool or an Ethical Morass?
Whenever Eric Karl Oermann has to tell a patient about a terrible prognosis, their first question is always: "how long do I have?" Oermann would like to offer a precise answer, to provide some certainty and help guide treatment. But although he's one of the country's foremost experts in medical artificial intelligence, Oermann is still dependent on a computer algorithm that's often wrong.
Doctors are notoriously terrible at guessing how long their patients will live.
Artificial intelligence, now often called deep learning or neural networks, has radically transformed language and image processing. It's allowed computers to play chess better than the world's grand masters and outwit the best Jeopardy players. But it still can't precisely tell a doctor how long a patient has left – or how to help that person live longer.
Someday, researchers predict, computers will be able to watch a video of a patient to determine their health status. Doctors will no longer have to spend hours inputting data into medical records. And computers will do a better job than specialists at identifying tiny tumors, impending crises, and, yes, figuring out how long the patient has to live. Oermann, a neurosurgeon at Mount Sinai, says all that technology will allow doctors to spend more time doing what they do best: talking with their patients. "I want to see more deep learning and computers in a clinical setting," he says, "so there can be more human interaction." But those days are still at least three to five years off, Oermann and other researchers say.
Doctors are notoriously terrible at guessing how long their patients will live, says Nigam Shah, an associate professor at Stanford University and assistant director of the school's Center for Biomedical Informatics Research. Doctors don't want to believe that their patient – whom they've come to like – will die. "Doctors over-estimate survival many-fold," Shah says. "How do you go into work, in say, oncology, and not be delusionally optimistic? You have to be."
But patients near the end of life will get better treatment – and even live longer – if they are overseen by hospice or palliative care, research shows. So, instead of relying on human bias to select those whose lives are nearing their end, Shah and his colleagues showed that they could use a deep learning algorithm based on medical records to flag incoming patients with a life expectancy of three months to a year. They use that data to indicate who might need palliative care. Then, the palliative care team can reach out to treating physicians proactively, instead of relying on their referrals or taking the time to read extensive medical charts.
But, although the system works well, Shah isn't yet sure if such indicators actually get the appropriate patients into palliative care. He's recently partnered with a palliative care doctor to run a gold-standard clinical trial to test whether patients who are flagged by this algorithm are indeed a better match for palliative care.
"What is effective from a health system perspective might not be effective from a treating physician's perspective and might not be effective from the patient's perspective," Shah notes. "I don't have a good way to guess everybody's reaction without actually studying it." Whether palliative care is appropriate, for instance, depends on more than just the patient's health status. "If the patient's not ready, the family's not ready and the doctor's not ready, then you're just banging your head against the wall," Shah says. "Given limited capacity, it's a waste of resources" to put that person in palliative care.
The algorithm isn't perfect, but "on balance, it leads to better decisions more often."
Alexander Smith and Sei Lee, both palliative care doctors, work together at the University of California, San Francisco, to develop predictions for patients who come to the hospital with a complicated prognosis or a history of decline. Their algorithm, they say, helps decide if this patient's problems – which might include diabetes, heart disease, a slow-growing cancer, and memory issues – make them eligible for hospice. The algorithm isn't perfect, they both agree, but "on balance, it leads to better decisions more often," Smith says.
Bethany Percha, an assistant professor at Mount Sinai, says that an algorithm may tell doctors that their patient is trending downward, but it doesn't do anything to change that trajectory. "Even if you can predict something, what can you do about it?" Algorithms may be able to offer treatment suggestions – but not what specific actions will alter a patient's future, says Percha, also the chief technology officer of Precise Health Enterprise, a product development group within Mount Sinai. And the algorithms remain challenging to develop. Electronic medical records may be great at her hospital, but if the patient dies at a different one, her system won't know. If she wants to be certain a patient has died, she has to merge social security records of death with her system's medical records – a time-consuming and cumbersome process.
An algorithm that learns from biased data will be biased, Shah says. Patients who are poor or African American historically have had worse health outcomes. If researchers train an algorithm on data that includes those biases, they get baked into the algorithms, which can then lead to a self-fulfilling prophesy. Smith and Lee say they've taken race out of their algorithms to avoid this bias.
Age is even trickier. There's no question that someone's risk of illness and death goes up with age. But an 85-year-old who breaks a hip running a marathon should probably be treated very differently than an 85-year-old who breaks a hip trying to get out of a chair in a dementia care unit. That's why the doctor can never be taken out of the equation, Shah says. Human judgment will always be required in medical care and an algorithm should never be followed blindly, he says.
Experts say that the flaws in artificial intelligence algorithms shouldn't prevent people from using them – carefully.
Researchers are also concerned that their algorithms will be used to ration care, or that insurance companies will use their data to justify a rate increase. If an algorithm predicts a patient is going to end up back in the hospital soon, "who's benefitting from knowing a patient is going to be readmitted? Probably the insurance company," Percha says.
Still, Percha and others say, the flaws in artificial intelligence algorithms shouldn't prevent people from using them – carefully. "These are new and exciting tools that have a lot of potential uses. We need to be conscious about how to use them going forward, but it doesn't mean we shouldn't go down this road," she says. "I think the potential benefits outweigh the risks, especially because we've barely scratched the surface of what big data can do right now."