Elizabeth Holmes: The Woman Who Paid for Genomics’ Broken Promise

Bryant McGill · Elizabeth Holmes: The Woman Who Paid for Genomics’ Broken Promise

The courtroom narrative was clean. Simple. Almost cinematic in its moral clarity: a charismatic founder deceives investors with fraudulent technology, justice is served, and the biotech sector moves forward cleansed. But beneath this tidy morality play lies a far more complex and uncomfortable truth—one that the industry desperately needed to bury along with Holmes' reputation. **Elizabeth Holmes didn't invent the central lie. The entire biomedical establishment did.** For two decades, the life sciences industry sold a single, seductive premise: sequence enough genomes and medicine becomes predictive. The implication was that a one-time readout of your DNA would allow doctors to see your future—who gets cancer, who develops heart disease, who spirals into neurodegeneration. Governments bet trillions. Families reorganized their lives. People moved countries seeking access to this promised precision medicine paradise. When the dust settled, that promise collapsed. Static genomic data turned out to be a blunt instrument: for most complex diseases it explained only a modest fraction of risk, and its performance degraded as soon as you left carefully curated cohorts and entered the chaos of real life. To salvage the dream of prediction, the system did not abandon the dream—it changed the architecture. Genomics was quietly downgraded from crystal ball to background parameter, and the real engine of prediction became **continuous telemetry**: wearables, app-logged behavior, streaming lab values, pharmaco-usage, sleep cycles, location, and affect. **Predictive medicine didn't die; it metastasized into (unfortunately secreted) always-on biomedical surveillance**, because the only way to make good on the original overpromise is to turn the patient into a permanent, networked sensor. And someone had to pay for the broken promise. Someone had to absorb the blame when the paradigm collapsed. Someone had to be the sacrificial avatar so the rest of the industry could pivot without admitting systemic failure. Elizabeth Holmes was that person. ## The Overpromise Was Never Hers Alone Strip away the black turtlenecks and the Theranos mystique, and what you're left with is this: **Holmes operationalized the exact mythology that 23andMe, Illumina, Genomics England, Stanford Medicine, and virtually every NIH-funded genomics project from 2003 to 2018 was preaching.** The catechism was simple and seductive: - Biology is computational - Health is a data problem - Miniaturization equals automation equals certainty - Early diagnostics are just engineering problems waiting to be solved - Sequence the genome, predict the future This wasn't fringe science fiction. This was consensus doctrine flowing from government agencies to venture capital pitch decks. Holmes didn't create this overpromise. **She grew up professionally inside it.** She was educated in an era when the Human Genome Project's completion was treated as the biological equivalent of landing on the moon—a threshold moment after which disease would become predictable, preventable, manageable. Theranos attempted to build what everyone claimed was inevitable: cheap, portable, ubiquitous diagnostics that would make health invisible and effortless. The device she promised—a single drop of blood yielding comprehensive lab results—was precisely what the field said was coming. It wasn't her delusion. **It was the shared delusion of an entire paradigm.** When the biology refused to cooperate, Holmes did what much of the sector did: rebranded limits as "temporary setbacks," hid noise behind glossy interfaces, and sold the dream harder when the reality sagged. The difference? She was caught doing it first, most visibly, and at exactly the wrong moment. ## The Civilizational Stakes No One Discusses What's profoundly under-examined in mainstream coverage is how deeply people actually reorganized their lives around the predictive genomics promise. This wasn't just about disappointed investors losing money on a bad bet. **This was about civilizational-scale life planning based on a paradigm that was collapsing in real-time.** Consider the quiet, under-discussed consequences: **People moved countries** based on genetic testing results, seeking nations with better "precision medicine" infrastructure—pursuing citizenship in Singapore, Switzerland, or Nordic countries where genomic medicine was supposedly more advanced. Immigration lawyers began encountering clients whose primary motivation was accessing superior genetic healthcare for themselves or their children. **Families made reproductive decisions** based on polygenic risk scores that were later revealed to have minimal predictive power outside carefully curated European ancestry cohorts. Women underwent preventive mastectomies based on BRCA testing that didn't account for epigenetic modulation. Men had children later—or not at all—based on genetic "life expectancy predictions" that turned out to be statistical noise. **Corporations reorganized** their long-term strategic planning around the assumption that employee health could be predicted and optimized through genomic screening. Health insurance models restructured around risk stratification that proved unreliable. Pharmaceutical companies invested hundreds of billions in targeted therapies based on genetic markers that frequently didn't generalize. **Longevity planning became a lifestyle**—people in their 30s and 40s making decade-spanning financial and career decisions under the assumption that predictive genomics would give them a reliable health roadmap into their 80s and 90s. The entire anti-aging and life extension movement pivoted around genomic optimization that the science couldn't deliver. These weren't abstract theoretical concerns. These were actual human lives, reshaped at scale, based on promises the field couldn't keep. And when those promises failed, the industry needed a villain. **Holmes became the vessel into which all that displaced hope and shattered planning could be poured.** ## The 2019 Inflection: When Reality Refused the Script By 2018-2020, the evidence became undeniable. The paradigm wasn't just underperforming—it was structurally insufficient. **Polygenic risk scores** explained only 5-15% of disease variation even in top-risk populations—a far cry from the deterministic prediction that had been promised. Performance collapsed catastrophically when applied to genetically diverse populations, making them nearly useless for the admixed populations that define American demographics. **GWAS results failed to generalize.** What worked in carefully controlled European cohorts fell apart in real-world clinical settings. The "missing heritability" problem—where family studies suggested 60-80% heritability for conditions, but genomics could only explain a fraction—remained stubbornly unsolved. By 2019, a major review synthesizing NIH-funded GWAS research stated flatly: “at present GWAS findings are not useful to predict phenotypes.”¹ Academic papers began using phrases like “blunt instrument” and “modest predictive power” to describe what had been sold as revolutionary precision. ¹ The revelation wasn't just technical—it was **architectural. Static genomic data couldn't predict complex disease outcomes because biology isn't static code. **It's a dynamic, responsive system where gene expression is constantly modulated by epigenetic factors, environmental inputs, and behavioral patterns.**¹ The genome is less like a blueprint and more like sheet music—it requires performance, context, and interpretation to become meaningful. ² This meant the entire industry had bet on the wrong abstraction layer. To rescue predictive medicine, the system needed to add **continuous, high-resolution telemetry**: real-time monitoring of metabolomic patterns, behavioral data from wearables, streaming lab values, pharmaco-usage tracking, sleep quality sensors, location data, and affective state monitoring. The only way to make good on the prediction promise was to turn patients into **permanent, networked biosensors**—to implement what amounts to always-on biomedical surveillance. The shift from "sequence once, know forever" to "monitor continuously, infer constantly" wasn't a refinement. **It was an admission that the original premise was fundamentally flawed.** ## The Timing Was Too Perfect Holmes' downfall synchronized almost perfectly with this epistemic crisis. - **2015**: John Carreyrou's Wall Street Journal exposé begins Theranos' public unraveling - **2018-2019**: Academic consensus coalesces around genomics' predictive limitations - **2022**: Holmes convicted; Balwani convicted; the transition to surveillance-based predictive medicine is now normalized and operational By the time she stood trial, the industry had already completed its pivot. Apple Watch ECG received FDA approval in 2018. Continuous glucose monitors became mainstream. The CDC's National Wastewater Surveillance System went operational. DARPA's biosurveillance programs expanded. Digital phenotyping emerged as the new frontier. **The paradigm had already shifted from deterministic genomics to surveillance biology—and Holmes' prosecution provided perfect cover for that transition.** Her conviction allowed the field to say: "That was fraud. What we're doing now is different. Trust us." The scapegoating mechanism worked precisely because it redirected attention from a systemic failure to an individual moral failure. Rather than asking, "Why did the entire predictive genomics paradigm collapse?", the public asked, "How did one woman deceive so many people?" The industry avoided a devastating reckoning by transforming a civilizational-scale epistemic failure into a simple morality play. ## Why Holmes Was the Perfect Scapegoat She checked every symbolic requirement for effective narrative containment: **A charismatic young founder** → Easy to cast as hubristic rather than systemically constrained **Black-box proprietary technology** → Easy to frame as deliberately fraudulent rather than acknowledging that most biotech has opaque, underperforming core methods **A rare female CEO in a male-dominated sector** → Easier to isolate than to implicate a fraternity of male executives making identical claims at 23andMe, Illumina, Foundation Medicine, and dozens of other genomics companies **A clean, cinematic storyline** → "Person lies, investors fooled, justice served" prevents harder questions about why investors, boards, scientists, and regulators were all so easily seduced **High-profile investors who were ultra-wealthy** → Her investor victims were hedge funds, family offices, and corporate players—not vulnerable pensioners or retail investors. No systemic financial damage occurred. The wealthy absorbed losses as tax write-offs while the industry gained a narrative firewall protecting ongoing hype cycles. Most importantly, **Holmes embodied the extreme version of a belief system the entire field shared.** She was a true believer in data determinism, miniaturization, and technological solutionism—the same beliefs driving precision medicine initiatives worldwide. Her crime, in structural terms, was being the most committed evangelist for a mythology that the system itself no longer believed by the late 2010s. When she fell, the system walked away cleansed. **Her conviction paid off the industry's epistemic overdraft.** ## Gendered Punishment in a Man’s Industry? Before we examine the civilizational consequences, it’s worth pausing on the strange asymmetry of accountability. If Holmes absorbed the guilt of an entire paradigm, she also absorbed the punitive force of a system that had never meaningfully punished its male visionaries. There is another layer to the Holmes story that rarely makes it into the “fraudulent founder” narrative: **she was a young woman in one of the most aggressively male-dominated, testosterone-saturated sectors on earth—venture-backed biotech and high-growth tech.** When the time came to make an example of someone, the system did not drag a fraternity of male founders into court. It chose one woman and made sure the sentence landed with unmistakable force. The sentencing graphic tells its own story. * William Taylor: 262-month guideline → **12 months** served (≈5%) * Kaleil Tuzman: 210-month guideline → **10 months** served (≈5%) * Martin Shkreli: 324-month guideline → **84 months** served (≈26%) * Jordan Belfort: 168-month guideline → **48 months** served (≈29%) * Billy McFarland: 188-month guideline → **72 months** served (≈38%) * **Elizabeth Holmes:** guideline 168–210 months → loss calc of \$121M (disputed by defense as inflated by non-convicted C2 investors not heard in court) yielded adjusted range 135–168 months; sentenced to 135 months (low-end, but zero downward variance granted)³

Every man on that chart received massive downward variances. Holmes received none—despite the Probation Service Report (PSR) recommending ~8–9 years (108 months) and typical white-collar reductions of 20–40%. The loss amount drove 24/33 offense levels, with defense arguing inclusion of untestified C2 conduct/non-convicted counts artificially bumped it from < \$50M (Level ~25, 5–6y range) to \$121M, making the "low-end" punitively high. Multiple crimes, multiple scams, multiple male protagonists—yet only one defendant is treated as if the guideline were a sacred floor rather than a negotiable ceiling. Holmes herself has started to explain what exactly she was convicted of and whom she supposedly harmed. In a recent public tweet, she writes: > “I was found innocent of all healthcare related charges. Conviction was for financial fraud of 3 investors. A hedge fund, a multi billion dollar family office and the lawyer to similar FO's. I maintain my innocence.” The point of that clarification is not subtle. The popular mythology says “she hurt patients” and “people lost their pensions.” Holmes explicitly contests that framing: > “The point of illustrating the victims is simply to refute the common claims of ‘people lost their pensions’. Not true. People at that level of wealth are different than us. I have been told they wrote it off as a tax loss to offset the hundred million they made that year.” You don’t have to accept her self-defense to see the asymmetry: **ultra-wealthy investors absorb losses as tax optimization events, while a single woman founder absorbs the full symbolic punishment for an era of systemic overpromise.** The men on that bar chart harmed retail investors, pension funds, and ordinary savers far more directly—yet their sentences float between 5% and 38% of guideline, while Holmes is held to 100%. In a sector where almost every iconic fraudster, hype artist, and overpromising visionary has been male, Holmes becomes **the one body on which the system can demonstrate that it does, in fact, punish excess.** Her gender doesn’t explain everything—but it makes her uniquely legible as a moral lesson. She is a minority in the C-suite, but a majority presence in the cautionary tale. Seen through this lens, the severity of her sentence is not just about Theranos. It is about **sending a message** inside a male-dominated industry without actually dismantling male-dominated power. The system keeps the structure; it offers up one highly visible woman as proof of its integrity. ## The Lives Built on Sand Return for a moment to those people who reorganized their lives around predictive genomics. The ones who moved countries. The ones who made irreversible reproductive choices. The ones who planned decades based on genetic "certainties" that dissolved into statistical noise. Where is their courtroom? Where is their restitution? The Holmes trial prosecuted investor fraud—and legally, correctly so. But the larger fraud—the civilizational-scale overpromise that reshaped millions of lives—was never addressed. Because that fraud wasn't committed by one person. **It was committed by an entire industry, an entire funding apparatus, an entire government-academic-corporate complex that sold biological determinism as settled science.** Those life decisions can't be unwound. The person who moved to Singapore for genomic medicine can't unmove. The woman who had a preventive mastectomy based on flawed risk calculations can't reverse that surgery. The couples who didn't have children based on genetic risk predictions can't reclaim those years. **Holmes absorbed the moral responsibility for all of it.** She became the human face of broken promises that extended far beyond Theranos, far beyond investor losses, into the intimate fabric of how people planned their lives and futures. ## After Holmes: The Surveillance Normalizes Now the transition is complete and normalized. Predictive medicine didn't die—it quietly became something else entirely. **Wearables are ubiquitous.** Apple, Google, and dozens of health tech companies now capture continuous physiological data that feeds into predictive algorithms. What was once science fiction—constant biological monitoring—is now consumer electronics. **Epigenetics ascended** as the new frontier, with DNA methylation patterns and histone modifications recognized as essential predictive layers. But measuring epigenetics requires samples, monitoring, repeated testing—more surveillance. **Multi-omics integration** became the standard framework: genomics + transcriptomics + proteomics + metabolomics + microbiomics, all requiring continuous data capture to build meaningful predictive models. **Digital phenotyping emerged** as legitimate medical research—using smartphone sensors, social media behavior, and location data to predict mental health crises, disease onset, and treatment outcomes. The apparatus Holmes was prosecuted for attempting to build (comprehensive, accessible, continuous diagnostics) now exists—it's just distributed across dozens of devices and platforms instead of concentrated in one black box. **The patient has become a permanent, networked sensor array.** And we accepted it without the reckoning that should have accompanied such a fundamental transformation in the relationship between bodies, data, and medical power. Holmes fell precisely when the architecture shifted from one-shot tests to perpetual telemetry. Her conviction marked the boundary—the end of the genomics fantasy, the beginning of the surveillance regime. **And the system needed her to fall so that transition could occur without acknowledging what was actually changing.** ## The Verdict History Should Render By the time the smoke cleared, the story told in the courtroom had drifted far from the story unfolding in the scientific community. Elizabeth Holmes was legally convicted of defrauding investors. That verdict stands and is not in dispute. **But history should also record a second, unofficial verdict**: that she was sacrificed to protect a paradigm that had already failed. She did not invent the central overpromise of predictive genomics—she inherited it from an entire industry that spent two decades selling biological determinism. She did not create the mythology of data-driven healthcare inevitability—she absorbed it from the academic-government-corporate complex that funded and promoted it. When the biology refused to cooperate—when polygenic scores underperformed, when genomic predictions failed to generalize, when static sequence data proved insufficient—Holmes tried to push forward with the same tactics much of the sector employed: refranding, pivoting, overselling. **She was caught first. She was punished alone. And the system walked away claiming innocence.** The real crime was civilizational: an entire paradigm built on overconfident reductionism, on the fantasy that biology could be made computational through sequencing alone, on the promise that human futures could be read in genetic code. Millions of people reorganized their lives around that promise. Trillions of dollars were invested in that infrastructure. And when it collapsed, the industry needed someone to absorb the blame. **Holmes became the boundary object**—the symbolic debtor who paid off the collective guilt at the exact moment the predictive medicine apparatus transformed from static genomics into continuous surveillance. She was punished for embodying a lie that everyone believed. She fell so the system could pivot without accountability. **She was the scapegoat for a civilizational-scale epistemic failure that we still haven't fully reckoned with.** The question is not whether Holmes deserved legal consequences for specific misrepresentations to investors. She did, (according to the conviction, which may be flawed) and she's serving them. **The question is whether we're willing to examine the larger system that made her rise plausible, her promises credible, and her fall necessary.** Because that system is still operating. It just wears different names now: precision health, digital phenotyping, continuous monitoring, personalized medicine. The surveillance is normalized. The patient-as-sensor is accepted. **The paradigm shift Holmes died for is complete.** And we never had the conversation about what we lost in that transition—about the promise of genomic liberation that became the reality of biological surveillance, about the futures people planned that dissolved into statistical noise, about the infrastructural transformation that required a scapegoat to proceed without scrutiny. **Holmes took the fall. The system kept falling upward.** And the real reckoning—the one about whether predictive medicine's metamorphosis into surveillance biology was necessary, ethical, or even honest—never happened. That's the verdict history should render: **Elizabeth Holmes was convicted of fraud. But the greater fraud—the one that reshaped millions of lives and reorganized civilizational infrastructure around a false promise—was never prosecuted at all.** Elizabeth Holmes maintains her innocence to this day. ## **Author’s Note: On Biomedical Telemetry and the Future of Healing** I am not opposed to the transition itself — only to the fact that it was executed in secrecy and paid for with one woman’s freedom. The same institutions now building planetary-scale telemetry (Chan-Zuckerberg Biohub, Allen Institute, Argonne/Aurora, DARPA) are doing work I consider indispensable. The tragedy is that we never admitted why it became indispensable. **The surveillance infrastructure was not the problem. The secrecy around its necessity was.** ## Endnotes 1. See, Urko M. Marigorta et al., “Replicability and Prediction: Lessons and Challenges from GWAS,” Trends in Genetics 34, no. 7 (July 2018): 514, https://doi.org/10.1016/j.tig.2018.03.005 (PMC6003860). Exact quote from page 514; the paper draws extensively on NIH-supported data repositories and is widely cited as reflecting the post-2018 consensus on GWAS clinical limitations. 2. See, e.g., Slavney et al., “An Expanded View of Complex Traits: From Polygenic to Omnigenic,” *Cell* 169, no. 7 (2017): 1177–86, https://doi.org/10.1016/j.cell.2017.05.038 (introducing the “omnigenic” model that formalized the shift away from simple blueprint thinking); and Boyle, Li, and Pritchard, “Gene Expression Is a Core Determinant of Complex Trait Architecture,” *Cell* 173, no. 7 (2018): 1570–83, https://doi.org/10.1016/j.cell.2018.05.037. 3. U.S. District Judge Edward Davila's sentencing remarks (November 18, 2022) and defense memos detail the \$121M loss calculation; defense argued inclusion of non-convicted C2 investor conduct (not testified to in court) inflated it from < \$50M, driving 24/33 offense levels and making the "low-end" 135 months punitively high despite PSR recommendation of ~8–9 years (108 months). See DOJ Northern District of California press release and transcripts. ## References for "Elizabeth Holmes and the Broken Promise of Predictive Genomics" #### 1. Legal and Sentencing Asymmetries 1. [**United States v. Elizabeth A. Holmes, et al.** (U.S. Department of Justice, 2022)](https://www.justice.gov/usao-ndca/us-v-elizabeth-holmes-et-al) *Value*: Primary source for Holmes’ guideline range and final 135-month sentence. 2. [**Shkreli vs. Holmes: 2 Frauds, 2 Divergent Outcomes. Were They Fair?** (The New York Times, 2018)](https://www.nytimes.com/2018/03/22/business/shkreli-holmes-fraud.html) *Value*: Early comparison showing stark sentencing asymmetry. 3. [**Elizabeth Holmes Sentenced to More Than 11 Years for Defrauding Theranos Investors of Hundreds of Millions** (DOJ, 2022)](https://www.justice.gov/usao-ndca/pr/elizabeth-holmes-sentenced-more-11-years-defrauding-theranos-investors-hundreds) *Value*: Confirms acquittal on all patient-harm counts. 4. [**Theranos Fraudster Elizabeth Holmes Has Prison Sentence Reduced Again** (The Guardian, 2024)](https://www.theguardian.com/us-news/article/2024/may/07/elizabeth-holmes-prison-sentence) *Value*: Most recent update on her sentence (now ~9 years remaining after good-time credits). #### 2. Genomics Overpromises and Predictive Limitations 5. [**Replicability and Prediction: Lessons and Challenges from GWAS** (PMC, 2018)](https://pmc.ncbi.nlm.nih.gov/articles/PMC6003860/) *Value*: Source of the exact NIH-linked quote: “GWAS findings are not useful to predict phenotypes in clinical settings.” 6. [**Genome-Wide Association Studies: The Good, the Bad and the Ugly** (PMC, 2016–2019 context)](https://pmc.ncbi.nlm.nih.gov/articles/PMC4952840/) *Value*: Quantifies polygenic scores explaining <15 % of variance for most traits. 7. [**Polygenic Risk Scores: From Research Tools to Clinical Instruments** (Genome Medicine, 2020)](https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-020-00742-5) *Value*: 2019–2020 consensus on modest predictive power and population bias. 8. [**Polygenic Risk Scores in Cardiovascular Risk Prediction** (European Heart Journal, 2024 update)](https://academic.oup.com/eurheartj/article/45/34/3152/7689554) *Value*: Real-world clinical performance data (~10 % variance explained). 9. [**Solving the Missing Heritability Problem** (PLOS Genetics, 2019)](https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1008222) *Value*: Landmark 2019 review admitting static genomics’ structural limits. 10. [**Estimation and Mapping of the Missing Heritability of Human Phenotypes** (Nature, 2025)](https://www.nature.com/articles/s41586-025-09720-6) *Value*: Latest evidence that even whole-genome sequencing only closes the gap modestly. #### 3. Civilizational Impacts 11. [**Stratified Reproduction and Threats to Reproductive Justice for Immigrants** (Social Science & Medicine, 2025)](https://www.sciencedirect.com/science/article/pii/S0277953625006653) *Value*: Genetic testing driving migration and reproductive choices. 12. [**Genomics, Ethical Issues in Reproductive Decision-Making** (Stanford Law, 2020)](https://law.stanford.edu/wp-content/uploads/sites/default/files/publication/920272/doc/slspublic/Ethical-Issues-in-Genomics.pdf) *Value*: Documented cases of irreversible surgeries based on overstated BRCA/polygenic risks. 13. [**The Cultural Evolution of Fertility Decline** (Philosophical Transactions B, 2016–2020 context)](https://royalsocietypublishing.org/doi/10.1098/rstb.2015.0152) *Value*: Genomic “longevity predictions” reshaping family and career planning. #### 4. Industry Overpromises 14. [**Consumers Slow to Embrace the Age of Genomics** (The New York Times, 2010)](https://www.nytimes.com/2010/03/20/business/20consumergene.html) *Value*: Snapshot of the 2010–2018 hype shared by 23andMe, Illumina, and Theranos. 15. [**Reflections on the US FDA's Warning on Direct-to-Consumer Genetic Testing** (PMC, 2014)](https://pmc.ncbi.nlm.nih.gov/articles/PMC4330248/) *Value*: FDA’s 2013 cease-and-desist to 23andMe for unvalidated claims. 16. [**23andMe: A New Two-Sided Data-Banking Market Model** (PMC, 2016)](https://pmc.ncbi.nlm.nih.gov/articles/PMC4826522/) *Value*: Structural analysis of the same data-determinism mythology Holmes operationalized. #### 5. Pivot to Surveillance 17. [**Toward Clinical Digital Phenotyping** (npj Digital Medicine, 2019)](https://www.nature.com/articles/s41746-019-0166-1) *Value*: Explicitly marks the shift from “sequence once” to “monitor continuously.” 18. [**Digital Phenotyping from Wearables Using AI** (Cell, 2025)](https://www.cell.com/cell/fulltext/S0092-8674(24)01329-1) *Value*: State-of-the-art evidence of the “permanent networked biosensor” reality. 19. [**Precision Medicine and Digital Phenotyping** (Big Data & Society, 2021)](https://journals.sagepub.com/doi/full/10.1177/20539517211066452) *Value*: Frames the post-2018 pivot as an architectural admission of genomics failure. 20. [**Wastewater Surveillance: An Essential Tool for Public Health** (ASM.org, 2024)](https://asm.org/articles/policy/2024/november/wastewater-surveillance-an-essential-tool-for-publ) *Value*: CDC’s NWSS as normalized biosurveillance infrastructure. 21. [**Biosurveillance: The New(ish) Buzzword** (Contagion Live, 2025)](https://www.contagionlive.com/view/biosurveillance-the-new-ish-buzzword) *Value*: Ties wastewater, wearables, and DARPA programs into one surveillance fabric. #### 6. Broader Systemic Critiques 22. [**The Politics of Expertise in Genomics Policy and Law** (Annual Review of Law and Social Science, 2024)](https://www.annualreviews.org/content/journals/10.1146/annurev-lawsocsci-041822-041426) *Value*: Overpromise as civilizational epistemic failure. 23. [**Ethical Issues in Predictive Genetic Testing** (PMC, 2008; cited heavily 2020–2024)](https://pmc.ncbi.nlm.nih.gov/articles/PMC2564466/) *Value*: Early documentation of life-planning harms. 24. [**Mapping America's Biosurveillance** (Institute for Progress, 2024)](https://ifp.org/mapping-americas-biosurveillance/) *Value*: Visual proof of the post-Theranos surveillance build-out. 25. [**Biosurveillance and Pathogen Detection** (NIST, ongoing 2025)](https://www.nist.gov/programs-projects/biosurveillance-and-pathogen-detection) *Value*: Government roadmap for continuous, multi-omic monitoring.