Debate over Artificial Intelligence often centers on dystopian fears of mass unemployment or catastrophic risk, while optimistic visions lean heavily on the prospect of curing cancer. Enthusiasts regularly invoke a future where cancer is effectively eliminated, using that scenario to justify large-scale Artificial Intelligence infrastructure spending and to counter mounting public and political backlash. Yet tying the value of Artificial Intelligence solely to near-miraculous medical breakthroughs sets an unrealistically high bar and obscures the more immediate and systemic ways it is already reshaping drug development.
For decades, the pharmaceutical sector has been trapped in “Eroom’s Law,” where drug discovery has grown slower and more expensive even as computing power followed Moore’s Law. The cost of developing a new medicine has roughly doubled every nine years, and the number of drugs produced per research dollar has steadily declined. Artificial Intelligence is starting to attack this productivity problem by improving speed, scale, and precision across the research pipeline, especially in cancer, which generates vast genomic, clinical, and imaging datasets no human team can fully integrate. New tools can detect subtle patterns, predict risk, match patients to personalized therapies, and screen billions of molecules, cutting down on the trial-and-error that currently dominates research and aiming to make cancer a preventable and fully treatable condition over time.
A Goldman Sachs analysis titled “Byte-ology: Quantifying AI’s value creation in drug development” frames drug development as an extremely costly funnel where it typically costs more than $2 billion and takes roughly 10 to 15 years, with fewer than 10 percent of candidates that enter preclinical testing ultimately reaching approval. Examining roughly 100 drug candidates developed using Artificial Intelligence, Goldman finds an overall success rate of about 10 percent compared with roughly six percent historically, an increase of nearly 60 percent, with the largest gains in Phase 1 and Phase 2 trials where most drugs usually fail. According to the report, Artificial Intelligence could reduce time to market by roughly 20 to 25 percent, shortening the process by about three years, largely by compressing early discovery by roughly 1.5 years and accelerating trials through faster patient recruitment and data analysis. It could lower total development costs by roughly 25 to 30 percent, including estimated reductions of roughly 28 percent in capital costs, and the combined effects of higher success rates, faster timelines, and lower costs could create between roughly $80 billion and $400 billion in industry value over the next decade. Investors increasingly view drug discovery, alongside healthcare, agriculture, energy, human capital, and education, as one of the most societally significant applications of Artificial Intelligence. While a single spectacular cure would capture public imagination, systematically reversing Eroom’s Law by restoring falling costs and rising research efficiency would represent a profound and durable shift in the economics and social value of pharmaceutical innovation.
