Balyasny: evaluating and governing frontier models at $38B scale
An interview with Balyasny Asset Management's chief AI officer on how a $38B multi-strategy firm puts frontier models into production. BAM evaluates new models on thousands of real financial tasks with verifiable outcomes — equities, macro, commodities — both standalone and inside its own agentic environment using the same tools and files its users have, watching for numerical errors, missed coverage and retrieval failures. On the relevant subset Claude Fable 5 scored 89.4% versus 86.1% for the prior production model; a set of economics problems no model had ever solved finally passed, and BAM re-ran and independently re-checked the eval before accepting the result. Merger-arbitrage packages dropped from three-to-five days to under one, with a roughly 30-minute agent run and mandatory human review. Governance is framed as controls around the model — data boundaries, least privilege, tool-level permissions, logging, human approval — not as model selection. Note this is vendor-published customer material.