Perplexity A.I. on AIDS as an in silico Ponzi scheme

- December 04, 2025

Create an essay about what a medical in silico Ponzi scheme would be.

A “medical in silico Ponzi scheme” would be a pathological version of today’s enthusiasm for computational medicine, where value and credibility are manufactured by ever more layers of models, projections, and virtual evidence rather than grounded clinical reality. The structure would echo a financial Ponzi scheme: early “returns” and apparent successes are paid for by continually enrolling new models, new surrogate endpoints, and new hype, while the underlying biological and clinical validity is never properly demonstrated. Once the flow of new inputs (funding, datasets, favorable publications, regulatory forbearance) slows or a real‑world test punctures the illusion, the structure collapses.

Core idea

In normal science, in silico methods are tools that extend and refine empirical work: simulations help generate hypotheses, reduce search space, and design better trials. In a medical in silico Ponzi scheme, that order is inverted. The simulation becomes the primary asset being sold—scientifically, financially, and politically—while empirical work is reframed as optional, delayed, or even unnecessary. The value proposition to investors, regulators, clinicians, and patients becomes: “Trust the model; reality will catch up later.”

Structural features

A medical in silico Ponzi scheme would likely show several interlocking traits:

The “principal” is model credibility. Instead of money being recycled to pay old investors, credibility and apparent validation are recycled among overlapping models, papers, and institutions. Each new layer cites the previous layers as if they were independent evidence.
Returns are reported as modeled outcomes rather than patient outcomes. Success is defined by metrics like predictive AUC, virtual trial endpoints, or modeled reductions in risk, while hard endpoints (mortality, morbidity, quality of life) remain unproven or weakly tested.
Risk is displaced into the future. Promoters argue that the full clinical validation “will come” once large enough datasets, next‑generation AI, or new regulatory frameworks are in place, so current decisions are based on promises about future validation, not present evidence.
Complexity serves as a shield. The models, data pipelines, and regulatory justifications become so intricate that only insiders can plausibly evaluate them, creating high barriers to criticism and making it easy to dismiss skeptics as simply not understanding the technology.

Incentive dynamics

For a Ponzi‑like logic to emerge, aligned incentives across multiple actors are needed:

Startups and pharma/biotech benefit from fast timelines, lower trial costs, and market differentiation through “AI‑driven” or “digital twin” branding. They are rewarded for impressive simulations more directly than for slow, messy patient outcomes.
Investors chase the promise of exponential scaling—once a model exists, it can be “applied” to many diseases, markets, or devices with minimal marginal cost, creating an illusion of near‑limitless upside.
Regulators under political pressure to support innovation may accept modeled evidence as a partial substitute for expensive, slow trials, especially if framed as more ethical (reducing animal or human experimentation).
Academic and clinical researchers gain prestige and funding by attaching to high‑profile modeling consortia, which may subtly pressure them to interpret ambiguous real‑world data as supportive rather than falsifying.

In such a system, everyone has short‑term reasons to keep believing in (and publicly endorsing) the in silico narrative, even as empirical support remains thin. That is precisely the incentive pattern that allows Ponzi structures to grow.

Mechanics of the “scheme”

The scheme operates by transforming uncertainty and assumptions into apparently solid, tradeable assets:

Model creation and over‑parameterization
A complex computational model of disease progression, drug response, or device performance is created, often using high‑dimensional data (omics, imaging, EHRs) plus prior biological assumptions. The space of adjustable parameters is large enough that at least some impressive “fits” can be found.
Selective validation and back‑testing
The model is “validated” primarily against historical datasets chosen or preprocessed in ways that smooth over anomalies. Failures are attributed to data quality or edge cases; successes are showcased in publications and investor decks.
Surrogate success metrics
Because real endpoints take too long, the model’s success is redefined in terms of surrogate markers (biomarkers, risk scores, virtual trial outcomes). These metrics are easier to optimize and look impressive in graphs and dashboards, but have uncertain relationships to patient benefit.
Layering and re‑packaging
New models are built on top of the original ones—meta‑models, digital twins, “platforms”—each claiming to add sophistication and address prior limitations. In practice, they inherit the original untested assumptions, but the sheer layering creates a sense of cumulative evidence.
Reinforcing narratives
Marketing, scientific reviews, and policy white papers repeat claims about the inevitability of in silico medicine, treating them as established trends rather than hypotheses. Over time, the narrative itself becomes a kind of collateral: “so many serious actors believe in this; it must be real.”
Substitution for trials
At the extreme, sponsors argue that simulated evidence plus limited real‑world data is sufficient for clinical decisions or regulatory approval, effectively substituting modeled reality for prospective trials. Patients and clinicians end up relying on an edifice that has never been truly stress‑tested.

Why “Ponzi” is more than a metaphor

Calling this pattern “Ponzi” is not merely rhetorical. The analogy is substantive in several ways:

Self‑referential growth: The scheme grows by feeding on its own prior claims. New models and new indications are justified because earlier ones were presented as successes, even if those “successes” were never independently verified.
Deferred reckoning: As in financial Ponzis, the problems only become undeniable when there is a liquidity crunch—here, that would be a moment when robust real‑world data directly contradict model predictions, or when regulatory or payer skepticism hardens.
Concealed risk to outsiders: Insiders may understand that the models are fragile and heavily assumption‑laden, but external stakeholders (patients, clinicians, policy makers) see only polished validations and consensus statements.
Dependence on continued inflows: The edifice stays afloat as long as there is continual inflow of fresh capital, datasets, and goodwill. Once skepticism becomes fashionable or funding tightens, the ability to keep re‑validating and re‑branding declines, and unresolved uncertainties surface.

Ethical and practical harms

A medical in silico Ponzi scheme would not just be an epistemic failure; it would have concrete harms:

Patients could be exposed to ineffective or harmful interventions justified by oversold models, especially in areas like oncology, personalized medicine, or device tuning.
Opportunity costs would be enormous: resources flowing into ever more elaborate simulations rather than into careful observational studies, mechanistic work, or robust trials.
Trust in computational methods more broadly could be damaged once the scheme is exposed, making it harder for genuinely rigorous in silico approaches to gain adoption.
Policy and public discourse could become polarized between uncritical technophilia (“trust the AI twin”) and equally uncritical technophobia, obscuring the possibility of a more balanced, evidence‑first use of modeling.

Guardrails and counter‑design

To prevent medical in silico innovation from drifting into Ponzi‑like behavior, several counter‑measures are essential:

Clear hierarchy of evidence: Explicit norms that simulations are hypothesis‑generating or trial‑optimizing tools, not substitutes for evidence on patient‑relevant endpoints.
Pre‑registration and falsifiability: In silico studies pre‑register hypotheses, endpoints, and validation plans, and commit in advance to accept or reject models based on predefined criteria.
Independent auditing: External methodologists and statisticians review models, code, and validation pipelines, with negative findings made publicly accessible.
Transparent uncertainty: Outputs must include robust uncertainty quantification, sensitivity analyses, and clear communication of where the model is unreliable.
Incentive realignment: Funding and regulatory pathways reward models that successfully predict and withstand real‑world tests, not just those that produce flashy retrospective fits or marketing narratives.

An essay about a “medical in silico Ponzi scheme” is ultimately an essay about how computational tools can be captured by pathological incentives. The same techniques that could genuinely improve trial design, reduce harm, and deepen understanding can also be arranged into a self‑referential, high‑status illusion. The distinction lies less in the mathematics and more in whether the community insists that reality—not the model of reality—remains the final arbiter.

Make the case based on Rebecca Culshaw's Substack that AIDS could be described that way.

A case can be made, using themes from Rebecca Culshaw’s Substack and related work, that the HIV/AIDS paradigm has elements of a medical “in silico Ponzi scheme,” in the sense that an elaborate computational and diagnostic edifice is repeatedly validated more by its own internal models and tests than by transparent, unequivocal clinical causation. Culshaw’s critique centers on how mathematical models, PCR‑based “viral load,” and shifting diagnostic criteria generate a self‑referential system whose credibility is continually rolled over rather than definitively proven in the way she believes a genuine infectious disease model should be. From that perspective, the “principal” of the scheme is not cash but belief in a specific HIV‑centric narrative, maintained by layers of in silico metrics and modeled risk that always seem to need more data, more parameters, and more time before they fully line up with messy clinical reality.charlesortleb.podbean+2

Culshaw’s core claims

Culshaw’s Substack and earlier writings argue that the HIV/AIDS story rests on tests and models that, in her view, are structurally incapable of delivering the kind of clear, causal proof that the public assumes. Key elements include:aidstruth+2

Questioning HIV isolation and causation standards (e.g., arguing that strict postulates were not met, and that “virus‑like particles” and indirect markers were reified into a distinct pathogen).catalog.nlm.nih+1
Emphasizing non‑specificity and cross‑reactivity of antibody tests, and the use of changing criteria for test positivity, which she sees as moving goalposts embedded in laboratory algorithms rather than stable clinical entities.charlesortleb.podbean
Criticizing viral‑load PCR as a measurement of fragments and amplification cycles rather than intact, infectious virus, turning a laboratory amplification signal into a quantitative stand‑in for disease burden.aidstruth+1

Taken together, these concerns frame AIDS as heavily mediated by computational proxies and interpretive rules—what counts as “HIV‑positive,” what copy numbers mean—so that the disease category is inseparable from the in silico infrastructure that defines and tracks it.catalog.nlm.nih+1

Role of mathematical modeling

Culshaw is particularly critical of early and influential mathematical models of HIV dynamics, which she argues were oversimplified, misinterpreted, and then used to justify long‑term treatment strategies that did not match real‑world outcomes. According to her critics, she misreads parts of the modeling literature, but even those rebuttals acknowledge that models were central in shaping the “hit early, hit hard” ethos and in framing HIV as a virus with extremely rapid turnover and inevitable progression, narratives that then fed into policy and clinical dogma. On the Ponzi analogy, those models function as the original “promissory notes”: they generate striking quantitative claims (e.g., enormous daily turnover) that attract scientific and political investment, even though their assumptions and parameter choices are not easily testable outside the modeling environment.pmc.ncbi.nlm.nih+2

As the field matured, new models were layered on top—incorporating latent reservoirs, sanctuary sites, and therapy resistance—with each generation presented as refinement rather than as a potential falsification of core assumptions about HIV’s causal role. In a Ponzi‑like pattern, perceived failures of earlier models are often reframed as reasons to build more complex ones, not to reconsider the underlying paradigm, so the theoretical “portfolio” expands even when empirical fit is patchy.pmc.ncbi.nlm.nih+2

Diagnostics and “virtual” disease

From Culshaw’s vantage point, HIV/AIDS is administered as a largely lab‑defined condition, where diagnostic status and prognosis depend on a chain of algorithmic and statistical decisions: cutoff values on ELISA/western blot or newer assays, PCR cycle thresholds, and CD4 counts interpreted against population distributions. She highlights that AIDS‑defining illnesses can occur in HIV‑negative individuals and that demographic and regional patterns of AIDS diagnoses are highly uneven, arguing this undermines the concept of a single, cleanly defined viral syndrome.charlesortleb.podbean+1

In that light, the disease resembles an “in silico” construct in the sense that:

The primary markers of risk and treatment success (antibody status, viral load trajectories, CD4 thresholds) are computational outputs of lab instruments and statistical cutoffs rather than directly observed transmissible entities or pathognomonic lesions.aidstruth+1
Apparent improvements—falling viral loads, rising CD4 counts—can be celebrated as therapeutic success even when the broader clinical picture (toxicities, long‑term outcomes, co‑morbidities) remains ambiguous or contested.xtramagazine+1

Within the Ponzi framing, each “good” lab number is akin to paying interest with freshly created script; the scheme looks solvent as long as stakeholders accept the script (the lab metrics) as sufficient evidence of underlying biological value.xtramagazine+1

Incentives and narrative maintenance

Culshaw’s Substack repeatedly foregrounds incentive structures: pharmaceutical profits from lifelong antiretroviral therapy, reputational investment by researchers and NGOs, and political capital built on AIDS as a defining global health crisis. She portrays a system in which:rebeccaculshawsmith.substack+2

Industry has strong incentives to promote lab‑driven management (routine testing, PrEP for large populations, combination therapies), which depends on continued belief in viral load and serostatus as decisive indicators.rebeccaculshawsmith.substack+1
Public health agencies and activists, having invested decades in an HIV‑centric narrative, treat dissent about causation or testing as dangerous denialism, thereby protecting the paradigm from the kind of open falsification and model replacement she advocates.pmc.ncbi.nlm.nih+2
Academics gain grants and prestige by refining models and biomarkers, not by dismantling the framework that supports them, much as participants in a financial Ponzi earn fees and status by keeping funds in motion.charlesortleb.podbean+1

In this account, the structure continues to grow because each new technology (more sensitive PCR, expanded screening, AI‑driven risk scores) is marketed as finally delivering on the original promises, while also creating fresh streams of data and revenue that help defer a hard audit of the core assumptions.rebeccaculshawsmith.substack+1

How the Ponzi metaphor applies and its limits

Describing AIDS as a “medical in silico Ponzi scheme” in Culshaw’s sense means emphasizing several parallels:

Self‑referential validation: clinical categories and treatment rules are justified by tests and models that themselves were never anchored in the kind of definitive isolation/causation standards she considers necessary, creating a closed loop of evidence.catalog.nlm.nih+2
Deferred reckoning: whenever discrepancies arise—AIDS‑defining illnesses in HIV‑negative people, long‑term non‑progressors, toxicity of early regimens—the response is to adjust models, redefine categories, or add new biomarkers, postponing a fundamental re‑evaluation.aidstruth+1
Asymmetric risk: insiders (researchers, companies, institutions) accrue benefits from maintaining the narrative, while patients bear the downside of any misclassification or treatment harm, analogous to late investors in a financial Ponzi absorbing losses.xtramagazine+2

At the same time, it is important to note that mainstream virology, clinical trials, and epidemiology present extensive evidence that HIV exists and plays a central causal role in AIDS, and they strongly reject the denialist framing. Antiretroviral therapies have been associated, in large randomized and observational studies, with substantial reductions in AIDS‑related morbidity and mortality, which most researchers interpret as real biological benefit rather than an artifact of laboratory metrics. Thus, while Culshaw’s Substack can be used to construct a Ponzi‑style critique of how models and in silico proxies structure the HIV/AIDS paradigm, this remains a minority, highly controversial position that conflicts with the prevailing scientific consensus.pmc.ncbi.nlm.nih+3