Martin Borch-Jensen writes a detailed analysis on the lack of the right datasets, which would hinder even highly advanced AI from producing breakthroughs in human longevity. AI's success in language and knowledge work cannot simply transfer to longevity research because biological data at the physiological and organismal layers where age-related diseases actually live is scarce, hard to generate, and slow to verify. Unlike protein folding or code, there are few corpora of highly-detailed longitudinal measurements; they also lack causal, task-relevant data for conditions like Alzheimer's or heart failure. What's more, clinical trial feedback loops take years. To unlock AI's potential in longevity, we need to deliberately generate the right kinds of data now: rich, multi-layer, longitudinal human studies which can be paired with faster experimental loops for causal hypothesis testing in the future.…