As AI Reshapes Drug Development, Global Access May Become the Real Test
A new critique asks who benefits when AI accelerates drug development. The answer may depend less on model quality than on whether the gains flow to diseases and regions that have historically been underfunded.
The enthusiasm around AI drug discovery often assumes that faster science automatically means broader benefit. Devex’s framing pushes back on that assumption by asking a more uncomfortable question: who actually gains when discovery becomes more efficient?
That question matters because the diseases most likely to attract AI investment are often those with large commercial markets and rich datasets. If left unchecked, the technology could widen existing disparities by making already well-resourced therapeutic areas even more productive, while neglected diseases remain under-supported.
There is also a distribution problem inside companies. AI can improve R&D productivity, but unless the resulting cost savings are reinvested into access, pricing, and global health priorities, the social impact may be limited. Efficiency is not the same as equity.
The best outcome would be a development model that uses AI to lower barriers for historically neglected areas, not just to optimize returns in lucrative markets. That will require policy pressure, funding incentives, and deliberate design choices from industry—not just better models.