AI Scientists Are Only as Good as Their Evidence: A Stratified Ablation of Proprietary Data and Reasoning Skills in Drug-Asset Valuation

Researchers demonstrate that AI agents' performance in drug-asset valuation is fundamentally limited by access to proprietary data rather than reasoning quality alone. A three-arm experiment shows that adding reasoning scaffolds and structured tools improves calibration but cannot overcome gaps in underlying evidence, with proprietary datasets enabling 96% recovery of expert valuations versus 38% for public-data-only systems.

This research challenges prevailing assumptions about AI capability bottlenecks in specialized domains. The study isolates three variables—base model quality, reasoning infrastructure, and data access—through controlled experimentation on pharmaceutical asset valuation, a task requiring deep domain expertise and real-time intelligence. The findings reveal a critical hierarchy: reasoning frameworks meaningfully improve consistency and objectivity metrics, yet remain insufficient without corresponding evidence density.

The experiment's stratified design across 13 assets with particular attention to long-tail cases strengthens its conclusions. System C's recovery of 96% of curated competitive records against System B's 38% demonstrates that proprietary intelligence—specifically curated pipeline, trial, and deal data—functions as an absolute ceiling on achievable accuracy. This suggests that organizations without access to proprietary datasets face structural constraints independent of model sophistication.

For AI-driven professional services, the implications are significant. Companies pursuing competitive advantage cannot rely solely on better prompting or reasoning architectures; they must invest in data aggregation, curation, and proprietary intelligence infrastructure. The introduction of "completeness-aware decision utility" as a metric (decision-quality × gold-coverage) acknowledges that accuracy without comprehensiveness produces false confidence in real-world applications.

Looking forward, this research will likely influence how organizations evaluate AI system procurement and development priorities. It suggests that larger models and advanced reasoning techniques represent incremental improvements in already-constrained systems. The next frontier involves determining whether proprietary data advantages are defensible long-term or whether competitive consolidation will eventually commoditize specialized knowledge bases.

• →Proprietary data access, not reasoning sophistication, sets the upper bound for AI agent performance in specialized domains like drug valuation.
• →Reasoning scaffolds improve calibration and discipline but cannot overcome fundamental gaps in underlying evidence availability.
• →System C achieved 96% recovery of expert valuations using proprietary data versus 38% for public-data systems, even with identical reasoning frameworks.
• →Perfect non-proprietary reports are theoretically capped at 50% effectiveness due to inherent coverage limitations of public information.
• →Decision utility metrics must account for both accuracy and evidence completeness to avoid overestimating real-world AI system reliability.