Interview
Evolution designed us to die fast; we can change that — Jacob Kimmel
- Aging is viewed as an evolutionary oversight that can be intervened upon, with potential outcomes including superintelligence if intelligence optimization were equally pursued, though natural selection may favor infectious disease resilience over longevity due to low population survival rates in high-hazard environments.
- Scientific approaches to extending healthspan include engineering the genome with antibiotic cassettes, editing genes like TRM5-alpha to restrict HIV, and utilizing gene duplication to discover new binding sequences, alongside epigenetic reprogramming to remodel the epigenome toward a youthful state using transcription factors.
- Epigenetic reprogramming faces challenges such as the inability of simple screen-and-prune methods to evaluate success, necessitating single-cell genomics to distinguish cell states and predictive models to navigate approximately 10^16 possible transcription factor combinations.
- Delivery mechanisms are evolving from lipid nanoparticles and viral vectors, which face limitations in targeting specific cell types or immune-privileged compartments like the brain, toward engineered cells (e.g., T cells) that patrol the body, with a prediction that this cellular delivery model will be dominant by 2100.
- Therapeutic strategies may involve one-time doses with persistent effects lasting decades, targeting "extra physiological" states for conditions like skin sagging, and achieving systemic benefits through the replacement of specific tissues like the liver or bone marrow, even if whole-body delivery is not yet achievable.
- Market dynamics anticipate a shift from the traditional payer system to direct-to-consumer and "pay for performance" reimbursement models, potentially reversing Arum's Law through AI scaling, with a total addressable market encompassing the entire global population and healthcare spending projected to decrease as prevention reduces intensive care utilization.
- Industry structures are expected to see traditional pharma firms relying on external biotech innovation for early R&D while focusing internal AI teams on later stages, with drug costs becoming more efficient per dollar over time as technologies go generic and value judgments are layered onto general-purpose biological pre-training models.