Productivity Paradox

The observation that despite unprecedented advances in science, technology, and investment, pharma R&D productivity has not improved — and may have declined. First articulated in this context by Mike Quigley at Session I.

The Problem

• The science has never been better; the output has rarely been worse
• Attrition is high, timelines are stretching, capital efficiency is declining
• AI-enabled reverse translation, platform optimization, and shorter DMTA cycles are necessary but not sufficient
• More science + more investment ≠ more productivity

Quigley’s Diagnosis

The bottleneck is the operating model, not the molecule. Optionality is destroyed early when biology is forced into a single-indication funnel. The fix is upstream of the bench — in decision systems, sourcing strategy, and continuous (not annual) portfolio management.

Cross-References

• Sanofi’s constellation view and agentic AI workflow as proposed solutions
• AI in drug discovery — necessary infrastructure but not a standalone fix
• All three Session I speakers point upstream of the bench from different vantage points