Khosla: AI floods drug discovery, but testing lags centuries behind

Vinod Khosla's remarks capture a fundamental tension now reshaping the pharmaceutical industry. Artificial intelligence has dramatically accelerated the early stages of drug discovery—the identification of promising molecular compounds and therapeutic targets—by orders of magnitude compared to decades-old computational and laboratory screening methods. However, this acceleration has exposed a systemic bottleneck: the downstream processes of laboratory testing, preclinical validation, and regulatory review operate on timelines that have barely changed in decades. Khosla's invocation of "18th-century" testing methods is intentionally provocative, signaling that modernizing these validation approaches is not merely a matter of efficiency but a competitive necessity for any player aiming to translate AI's discovery advantage into real-world therapeutics. The implication is clear: without parallel innovation in testing infrastructure, even the most impressive surge in drug candidates will fail to translate into faster treatment availability.