Every delay in developing a new medicine is measured in lives. A month lost in analysis is a month that patients wait for therapies that could help them. A year spent navigating technical bottlenecks is a year that families hope for answers that don’t come.
The frustrating part is that we’ve never been more capable. Sequencing costs have collapsed by six orders of magnitude. Gene editing lets us test biological function directly. AI can model protein structures and cellular pathways with remarkable precision. Population-scale datasets—molecular, phenotypic, real-world—are growing exponentially and becoming more accessible. The scientific tools available today surpass anything the field has ever known.
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Yet the opposite is happening. Despite unprecedented capability in the lab, the productivity of life sciences has been declining for decades. The number of new FDA approvals per billion dollars spent has halved every nine years since 1950. There’s even a name for it in life sciences—Eroom’s Law, which is Moore’s Law spelled backward. We have more talented people, more advanced methods, and more data than ever before. But the system is producing fewer medicines, not more.
The Universal Problem: Specialization’s Hidden Tax
A key root cause is visible across every knowledge-intensive field: specialization.
As fields advance, they subdivide. Oncology becomes immuno-oncology, targeted therapy, precision diagnostics. Data science becomes bioinformatics, statistical genomics, machine learning engineering. Each subspecialty develops its own language, methods, and systems. This depth is necessary—it’s how we push the boundaries of what’s possible.
But it creates a bottleneck. There are fewer and fewer people who are “multi-lingual”—who can bridge between the oncologist and the bioinformatician, the clinical team and the data engineers, the scientific question and the technical execution.
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