Measures of the severity of cognitive impairment or parkinsonism are the usual endpoints in clinical trials for Alzheimer’s disease (AD) and Parkinson’s disease (PD), but are critically hampered by their lack of disease sensitivity and specificity. Due to the high failure rate of clinical trials, the rate of regulatory approval for efficacious new drugs has stagnated in the past few decades, with the gap between basic science discovery and clinical application metaphorically termed the “Valley of Death”. While the causes for this are probably multiple and complex, the usage of biomarkers as surrogate endpoints, particularly when they are molecularly-specific for the disease, has achieved some success in cancer trials, and it is likely that neurodegenerative disease trials would benefit from the same approach. As dementia and parkinsonism are not disease-specific clinical syndromes, both AD and PD trials have been flawed by reliance on clinical diagnosis and clinical endpoints. Clinical improvement has been a requirement for regulatory approval, but molecularly-specific biomarkers should improve both diagnostic accuracy and tracking of disease progression, allowing quicker screening of drug candidates. However, even when a molecularly-specific biomarker is found, such as amyloid imaging for AD, it may not reflect the entire extant molecular disease repertoire and may not serve equally well in the different roles of preclinical detection, diagnostic confirmation and surrogate endpoint, necessitating the usage of two, three or more biomarkers, deployed in series or in parallel.