Evolution Didn’t Design You for Long Life — Can Science Change That?

• Evolution only needed you to reach reproduction — Natural selection favored traits that carried humans into their childbearing years and allowed them to raise children, but it applied little pressure beyond that point. As Kimmel explains, in human and primate history, the daily chance of dying (what he called the “baseline hazard rate”) from infection, predators, or accidents was extremely high.

If most lives ended around 40, there was no evolutionary incentive to shape traits that would keep you vigorous at 60. “The number of individuals in the population that are going to make it later in that lifespan, where using some of your evolutionary updates to try and push your lifespan upward, is relatively limited,” Kimmel said.⁴

• This high hazard rate also influenced traits like intelligence — Longer childhoods made it possible for humans to develop larger, more capable brains, but stretching adolescence too far carried the risk of dying before reproduction. This is reflected in your fluid intelligence, the ability to reason, solve new problems, and think flexibly without relying on prior knowledge or experience,⁵ which peaks around your 20s or 30s.

Evolution optimized for cognitive prowess when you were most likely to contribute to the group, not later in life. Mathematical discoveries often occur before age 30, suggesting your brain’s peak aligns with the age of maximum population contribution during evolutionary history.

• Evolution may have even favored shorter lifespans — Kimmel explains that, from the perspective of the “selfish gene,” older individuals who are less fit and still consuming resources could reduce a group’s overall survival.

If you live longer but contribute fewer calories or gather fewer resources than younger members, your extended presence actually lowers the group’s fitness. In this sense, evolution tends to favor turnover, giving younger and more productive individuals the chance to propagate genes more effectively. According to Kimmel:

“There is a notion by which a population being laden demographically with many aged individuals, even if they did have fecundity persisting out some period later in life, is actually net negative for the genome’s proliferation and that really a genome should optimize for turnover and population size at max fitness.”⁶

• Longevity sits within the constraints on evolution’s optimization process — Kimmel describes the genome as a set of parameters and natural selection as an optimizer with limits. Mutation rates need to stay low to prevent catastrophic errors such as cancer, and small population sizes restrict how many genetic variants can be tested.

At the same time, your ancestors were locked in a constant battle with infectious disease, which absorbed much of evolution’s attention. These constraints left little room to fine-tune traits related to longevity, even if longer life might have offered some benefit.

• Microbes produce antibiotics through a unique evolutionary advantage — With vast population sizes and extremely high mutation rates, bacteria and fungi engage in chemical arms races, churning out molecules like antibiotics to outcompete rivals. This process allows microbes to rapidly adapt, producing diverse compounds that target specific competitors in their environment.

• Humans, by contrast, could never evolve along this path — Our mutation rates need to stay relatively low in order to protect the stability of our complex genomes. Rapid mutation at microbial levels would lead to catastrophic consequences, most notably uncontrolled cancer. This constraint means that while microbes thrive on variation, mammals depend on genetic stability to survive from one generation to the next.

• Because of these biological limits, humans developed a different defense system — Instead of producing chemical antibiotics internally, you evolved an adaptive immune system capable of learning and remembering threats. This approach provides flexibility without relying on high mutation rates. It also allows your body to respond to a wide variety of pathogens across your lifetime, even as they change and adapt.

• Your DNA still carries the marks of past battles with pathogens — Over millions of years, infectious diseases shaped survival, and the genetic record shows the defenses your ancestors developed against those threats. These remnants serve as evidence of how strongly microbes directed human evolution, even when the pathogens themselves disappeared long ago. Kimmel points to one striking example:

“We have a gene called TRIM5alpha. It actually binds an endogenous retrovirus that is no longer present, but was at one point actually resurrected by a bunch of researchers. It was demonstrated that this is the case. We have this endogenous gene which basically fits around the capsid of the virus like a baseball in a glove and prevents it from infecting.”⁸

• The epigenome explains how identical DNA produces different cell types — For instance, a kidney cell and an eye cell carry the same genetic code, yet they perform distinct tasks because the epigenome programs them differently.

• The main levers of this system are transcription factors — These are proteins that bind to DNA and direct gene activity, turning certain genes on and others off. Kimmel describes them as conductors of an orchestra — they don’t perform the functions themselves but determine which instruments play, when they enter, and how they interact. In the same way, transcription factors set the rhythm of cellular behavior.

• Epigenetic reprogramming restores youthful patterns of gene activity — With age, the epigenome drifts, leading to weaker cell performance. By steering transcription factors in specific ways, the goal is to return aged cells to a state where they function as effectively as they did when they were young, without altering the DNA sequence itself. For example, a liver cell would remain a liver cell but regain the ability to clear toxins efficiently, and an aged T cell would recover its capacity to fight infections.

• Kimmel contrasts this with the Yamanaka factors — Discovered by scientist Shinya Yamanaka, these factors strip away a cell’s specialized identity, turning it into a blank slate that could become any cell type. Kimmel notes that this process, while powerful, carries risks because it disrupts the cell’s role in your tissues.

• The size of that space is one of the biggest scientific challenges — There are thousands of transcription factors, and when you consider the possible combinations, the number of potential interventions rises into the trillions. Testing every possibility in the lab is impossible, which is why computational tools have become essential.

• This is where computational tools come into play — Machine learning models can analyze massive amounts of experimental data and help pinpoint which transcription factor combinations are most promising to test. Instead of working blindly through endless options, researchers can use this technology to chart a focused path.

• Lipid nanoparticles (LNPs) — These “fat bubbles” that resemble cell membranes are taken up by tissues like the liver, which naturally absorb fat. They are the same technology used in mRNA vaccines, where they carry RNA into cells. In reprogramming, they can deliver RNA instructions for transcription factors.

However, Kimmel points out that LNPs have physical limits in how they travel through the body, making them unlikely to serve as a lasting solution. I’ve also covered their risks before, including in the context of mRNA shots, in “HIV mRNA Vaccines Continue to Fail in Clinical Trials.”

• Viral vectors — Another common method borrows from viruses, which have evolved specifically to enter cells. One example is AAV (adeno-associated virus), which can carry DNA payloads into certain cell types. Kimmel likens AAV to a small delivery truck — it can bring in whole genes but has limited cargo space.

Researchers engineer these viral sequences further to restrict where the genetic payload is active. However, viral vectors always carry some degree of immunogenicity, raising risks of immune reactions and toxicity.

• Future solutions may resemble the systems your own body already uses — The immune system already has cells that patrol tissues, sense problems, and release targeted responses. These engineered immune cells could eventually take on the role of couriers for reprogramming therapies, delivering them with precision and safety that current methods cannot achieve. According to Kimmel:

“Ultimately, we’re probably going to have to solve delivery the way that our own genome solved delivery. We have the same problem that arose during evolution … We have cell types in our body, T cells and B cells, which are effectively engineered by evolution to run around, invaginate whatever tissues they need to.”¹¹

• Computational models help reduce the trial-and-error bottleneck — A major challenge in drug discovery is the dependence on trial and error in living systems. Each experiment is costly, slow, and narrow in scope, leaving progress constrained by the physical bottlenecks of the lab.

Kimmel explains that accurate computational models could shift much of this process into silico, allowing researchers to simulate biology with far greater speed and scale than traditional experiments.

• What virtual cells are — Virtual cells are computer-based simulations of how real cells behave. By capturing how genes are expressed, how proteins interact, and how pathways respond, they create a digital environment where interventions can be tested.

In practice, this means scientists could simulate how transcription factors or other therapies change gene activity and cell function, then filter out unpromising approaches before moving to the lab.

• Virtual cells expand what can be tested — The benefit is not only speed but also the ability to explore ideas that would be impractical in physical labs. Entire classes of hypotheses could be tested computationally, widening the scope of discovery beyond what current resources allow. This doesn’t eliminate the need for lab work, but it means that only the most promising interventions reach that stage, saving time and cost.

• Kimmel frames this shift as essential to breaking free of Eroom’s Law — Without it, drug development will remain constrained by slow, expensive cycles that hinder innovation. With it, medicine could move toward a future where discovery scales more like computing, driven by the ability to model biology in silico.

• One model already under discussion is pay-for-performance — This is where the cost of a therapy depends on its real-world effectiveness for you. For long-lasting treatments like those targeting aging, insurers face challenges because patients may switch providers before they experience benefits. Linking payment to measurable health improvements ensures you receive therapies that work while addressing payers’ concerns about covering high upfront costs.

• Another shift could involve direct-to-consumer access — In this model, drugs might be made available to patients in ways that resemble consumer products, bypassing some of the traditional channels that rely heavily on insurers and intermediaries. This approach could simplify your access to innovative drugs, particularly for chronic conditions or aging-related therapies.

• The development of these therapies often begins with small biotech firms — “The industry has sort of bifurcated where smaller biotechs like ours take on most of the early discovery,” Kimmel said. Meanwhile, larger firms step in later to manage clinical trials, regulatory approval, and global distribution. This division of labor reflects how risk and expertise are distributed in the sector.