{
  "id": 14515639,
  "title": "AI may double human lifespans",
  "url": "https://urgent.news/2026/10/14/ai-may-double-human-lifespans",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-14T18:47:00.000Z",
  "source": {
    "name": "Fast Company",
    "slug": "fast-company",
    "url": "https://www.fastcompany.com/91622314/ai-may-double-human-lifespans"
  },
  "original_language": "en",
  "account": "Longevity research is gaining new tools and techniques with the help of artificial intelligence (AI). Two recent studies from our team and academic partners highlight this progress. In Cell, we presented open tools to evaluate AI models and assist researchers in pinpointing potential drug targets in aging biology. Meanwhile, in Nature Biotechnology, we examined how blood-based aging assessments could be integrated into a clinical trial of an AI-created drug. Although these studies do not definitively state that a drug will extend anyone's lifespan, they point towards a promising future for drug development. AI can help identify potential therapies and investigate their effects on aging biology alongside the diseases they aim to treat. For business leaders, the opportunity lies in facilitating this connection. AI can augment the discoveries and measurements made by scientists, but rigorous experiments and clinical trials are crucial to determine which advancements truly improve health. The anticipation of AI potentially doubling human lifespans is an ambitious goal, yet one that could become a reality with the right foundational tools. Drug discovery depends on two distinct clocks: the computational clock and the biological clock. The computational clock operates swiftly, enabling AI to analyze biological data, identify potential drug targets, generate chemical structures, and assist scientists in selecting experiments. Conversely, the biological clock moves at a slower pace. Synthesizing and testing molecules, establishing whether they reach the intended tissue and act as expected, and conducting human studies with patient recruitment and follow-up are all time-consuming processes. Our experience with rentosertib, a drug developed for idiopathic pulmonary fibrosis, exemplifies this distinction. The drug was created using AI to identify a biological target and design a molecule against it, with a preclinical candidate reached in just 18 months. However, this marked a discovery milestone, not a finished medicine. Additional laboratory studies and clinical trials followed. Each stage answered questions that the previous one could not, but for executives and investors, it is essential to measure progress against the appropriate milestones. A faster prediction, a promising experiment, and a successful clinical trial represent different achievements. Presenting them on the same timeline can create unrealistic expectations regarding when patients will benefit. The Nature Biotechnology analysis applied six proteomic aging clocks to blood samples from a subset of rentosertib trial participants. These models estimate biological age based on patterns of circulating proteins. The treatment showed treatment-associated shifts toward younger predicted profiles, which is intriguing but requires further investigation. The short observation period and involvement of only 42 people with a serious lung disease limit the scope of the findings. A change in proteins may reflect several processes, including those related to the disease itself. Thus, distinguishing between a biomarker and a clinical outcome is vital to prevent misconceptions. AI has the potential to enhance clinical trials, streamlining protocol design, recruitment, and data review to reduce delays. However, efficiency alone cannot answer every biological question. Rare adverse effects may only surface after enough people receive a medication, and durable benefits often require long-term follow-up. Separating delays that technology can eliminate from observations that science still needs is crucial. While academic researchers provide essential insights into mechanisms, measurement, and experimental validation, partnerships should focus on both commercial milestones and the underlying scientific foundation. Companies should set clear success criteria before claiming any longevity benefits. Defining the population, outcome, and evidence required to establish these claims is essential. Transparency in publishing methods and limitations is equally important. For investors, it is crucial to assess whether a company can consistently transform predictions into credible experiments and clinical evidence. The number of molecules generated by AI is less important than the quality of the decisions that follow. Despite the optimism surrounding radical life extension, progress is likely to be incremental, building upon successive generations of medicines, diagnostics, and other technologies that prevent disease and restore function. AI can aid in the development and integration of these advances. Our program demonstrates an encouraging clinical signal and a new research question about aging. The next step is to rigorously test the findings.",
  "summary": "AI is giving longevity research a new way forward: better tools to identify potential interventions, and better ways to investigate their effects in people. Two recent papers from our team and academic collaborators illustrate that progression. In Cell , we introduced open tools to evaluate AI models and help researchers identify potential drug targets in aging biology. In Nature Biotechnology ,…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}