Investors & Media

Linear versus Systems: AGI and Aging

Artificial General Intelligences (AGIs) are AI systems that could match human abilities across all cognitive domains, not just in managing narrow, specialized tasks. The emergence of AGI coincides precisely with Telocyte's ongoing transition from theory to clinical reality. This timing reveals a deeper parallel: both AGI and our approach to aging represent fundamental shifts from reductionist thinking to systems understanding.

Current AI systems demand massive computational power to perform specific tasks, while biological intelligence emerges from low-power neural networks through principles of efficiency, plasticity, and multi-scale integration. Some researchers already see emergent general capabilities in AI models; others argue that AI lacks any of the emergent understanding and adaptive learning that define biological intelligence.

From Machines to Processes

For centuries, we have relied on mechanical analogies to understand as complex machines like clocks, computers, or mechanical systems with fixed parts. These metaphors have dominated both AI development and aging research, leading to linear, mechanical approaches in both fields.

Aging itself was seen through an equally constraining lens. We viewed aging as simple entropy, focusing on biomarkers, and assuming an inevitable decline. An aging body was like a machine that inevitably wore out over time: the most we could do was replace a few failing parts. But living organisms aren't machines at all. They're more akin to flames or flowing streams, maintaining function by continually recycling themselves. Machines can be switched on and off, while organisms must work constantly to stay alive… or they die. Relentless turnover is the maintenance that overrides entropy. Organisms exist only because they never stop repairing themselves in a ceaseless and active process of living.

An organism's identity isn’t static. It comes from the dynamic process of recreating, maintaining, and healing its overall pattern. Machines have fixed parts; organisms constantly and dynamically rebuild their parts. Understanding organisms as active processes rather than static objects explains why life – and every cell in your body – has survived for four billion years. Only when active maintenance slows during our decades of life, does aging begin to finally take its toll.

This shift—from machine to process, from static biomarkers to dynamic cascades—transforms how we understand both aging and its reversal. Aging isn't entropy winning; it's the disruption of the delicate balance between entropy and cellular maintenance. Our direct telomere-lengthening approach embodies this systems thinking. Rather than targeting downstream biomarkers mechanically, we reset the fundamental cellular processes that maintain the pattern of cellular function.

AGI: partner, not replacement

AGI holds real promise for longevity science, but also has crucial limitations. At the end of the 19th century, aeronautics meant hot air balloons. The major technical challenges focused on better envelope materials to prevent buckling, lighter and more efficient heat sources, improved directional control, and safer landing systems. Had AGI existed then, it would have been asked to design the perfect hot air balloon. But the question was wrong.

The breakthrough required asking not "how do we perfect hot air balloons?" but "how do we achieve heavier-than-air flight?" Ask the wrong question and you get the wrong solution, no matter how perfect that solution might be within its limited scope.

AGI excels at answering questions but cannot make the intuitive leap to wonder if we've asked the right question in the first place. If we want a protein, AGI can identify the optimal protein—but is a protein the best solution? If we want a better antibiotic, AGI can design novel antibiotics—but is an antibiotic the optimal therapy? If we want to target aging biomarkers, AGI can locate optimal interventions—but are biomarkers the right target?

Finding drugs to target biomarkers entirely misses the point. Biomarkers are effects of aging, not causes. Aging is not a collection of measurable markers but a complex, dynamic cascade of biological events that produces those markers. We need AGI to help answer our questions, but we need biological intelligence to ask the right questions.

For Telocyte, AGI will prove invaluable for literature synthesis, regulatory navigation, trial design optimization, and compressing timelines from animal studies to human trials. It will help us navigate biological complexity and create high-fidelity digital simulations for clinical investigations. But the insight that led us to target telomere length to reset cellular gene expression—that fundamental systems view of aging—required biological intelligence asking the right question.

AGI can get us there faster, but it can't tell us where to go.

The Irreplaceable Human Element

AGI forces us to examine what makes biological intelligence unique. The critical distinction may not be AGI's ability to answer questions, but the human capacity to wonder if we're asking the right questions at all. Innovation has never been about finding the optimal stone for a stone axe, but asking why we should use stone at all.

This partnership model will define the future: AGI managing complexity and finding efficient answers, while biological intelligence drives innovation by questioning what problems we should solve. AGI can accelerate clinical development and regulatory timelines, but human intelligence must determine what needs developing and regulating. The human elements -- compassion, intuition, and the ability to question fundamental assumptions – remain irreplaceable.

Humans define the mission; AGI helps accomplish it. Our goals remain profoundly human; our methods will be optimized through artificial intelligence.

Values versus intelligence

We stand at a remarkable convergence: AGI emerging as Telocyte prepares for clinical trials. We possess unprecedented tools to navigate complexity, but the wisdom to deploy those tools effectively depends on biological intelligence. We need the courage to question our assumptions and the compassion to ask not just how we can solve problems, but whether we truly understand what those problems are.

Technology must serve humanity's deepest values. AGI neither defines nor changes our mission—it ensures efficiency and makes success more attainable. The future belongs to those who understand both the profound power of artificial intelligence and the irreplaceable role of human wisdom in defining what that power should accomplish.