The ultimate goal of precision medicine is a unique drug for each patient. However, this N-of-1 model directly conflicts with the current economic and regulatory system, which incentivizes developing drugs for large populations to recoup massive R&D and approval costs.

Liz Parrish pushes back against claims that her work is reckless or too fast. She contends her team moves too slowly, spending years studying each therapy. Their pace only appears rapid in comparison to the extremely slow processes of mainstream medical research and regulation.

Eroom's Law (Moore's Law reversed) shows rising R&D costs without better success rates. A key culprit may be the obsession with mechanistic understanding. AI 'black box' models, which prioritize predictive results over explainability, could break this expensive bottleneck and accelerate the discovery of effective treatments.

The high failure rate in drug development is analogous to trying to repair a car with no mechanical knowledge—it's just "banging on different parts." This highlights the industry's need to shift from observing correlations to understanding the fundamental biological mechanisms of disease.

Parrish suggests that when analyzing criticism from the scientific community, one must consider financial motives. A researcher's funding and career are built on perpetuating research, not on translating it into real-world application, creating an inherent bias against moving too quickly.

While the wealthy can access expensive protocols involving diagnostics and lifestyle optimization, these offer only marginal benefits. True, effective longevity will not come from this but from validated, mass-produced biotech drugs that target the core mechanisms of aging.

Our ability to generate and test therapeutic hypotheses in silico is rapidly outpacing the slow, expensive conventional clinical trial system. Without regulatory reform, the pipeline of promising drugs will remain stuck, preventing breakthroughs from reaching patients. The science is solvable; the system is not.

Despite AI's power, 90% of drugs fail in clinical trials. John Jumper argues the bottleneck isn't finding molecules that target proteins, but our fundamental lack of understanding of disease causality, like with Alzheimer's, which is a biology problem, not a technology one.

The fastest, cheapest path to drug approval involves showing a small survival benefit in terminally ill patients. This economic reality disincentivizes the longer, more complex trials required for early-stage treatments that could offer a cure.