Click here to watch a video of this episode.
Drug development workflows only get heavier, and subtractive change requires predictivity and decision-grade evidence that can survive regulatory science scrutiny. In this episode we talk about how AI and translational science can earn the right to delete steps, not decorate them. The tension is uncomfortable: are you willing to remove something you have always done, or will you just add another layer and call it progress? We introduce the idea of a Predictivity Ledger, a simple way to make miss rates visible and force clarity about what is being claimed, in what context, and with what failure modes. Deletion is not a vibe. It is a governance decision with receipts. Takeaway: pick one legacy step, define deletion criteria, and start logging misses like they cost time and money, because they do.

• (00:00) - Episode purpose, resolutions, and AI adoption risks
• (02:00) - Subtractive strategy and replacement opportunities in practice
• (04:00) - Evidence authority, familiarity, and higher validation bars
• (07:00) - Add-on endpoints and why adoption stalls
• (08:00) - Decision bias and the need to challenge interpretation
• (10:00) - Predictivity ledger components for accountable decisions
• (12:00) - AI capabilities, limits, and accountability requirements
• (14:00) - Administrative AI example and subtractive realization questions
• (18:00) - Process efficiency versus biology, fast-fail critique
• (25:00) - Breaking silos, expert finding, and organizational knowledge
• (29:00) - Historical knowledge, foundational literature, and AI leverage
• (33:00) - Closing remarks and next episode sign-off