In times of war, the market's direction is dictated more by geopolitical events and military strategy than by traditional financial metrics. Understanding a conflict's potential duration (e.g., a swift operation vs. a prolonged war) becomes the most critical forecasting tool for investors and risk managers.

Systematic models don't attempt to forecast unpredictable shocks like policy changes. Instead, they build portfolios with 'guardrails'—diversifying away concentrated macro risks like sector or country bets—to ensure resilience and avoid being badly damaged by any single event.

Financial crises are rarely caused by risks everyone is watching, like inflation (known knowns). The true danger comes from unforeseen events (unknown unknowns) like 9/11 or the Lehman collapse, which aren't priced into risk models and cause systemic panic.

Even in hyper-quantitative fields, relying solely on logical models is a failing strategy. Stanford professor Sandy Pentland notes that traders who observe the behavior of other humans consistently perform better, as this provides context on edge cases and tail risks that equations alone cannot capture.

Single-factor models (e.g., using only CPI data) are fragile because their inputs can break or become unreliable, as seen during government shutdowns. A robust systematic model must blend multiple data sources and have its internal components compete against each other to generate a reliable signal.

As quantitative models and AI dominate traditional strategies, the only remaining source of alpha is in "weird" situations. These are unique, non-replicable events, like the Elon Musk-Twitter saga, that lack historical parallels for machines to model. Investors must shift from finding undervalued assets to identifying structurally strange opportunities where human judgment has an edge.

During crises, Blankfein’s team ignored predictions about likely outcomes. Instead, they focused exclusively on identifying all possible (even low-probability) negative events and creating contingency plans. This readiness allowed them to react faster than competitors when a tail risk event actually occurred.

Advanced AIs, like those in Starcraft, can dominate human experts in controlled scenarios but collapse when faced with a minor surprise. This reveals a critical vulnerability. Human investors can generate alpha by focusing on situations where unforeseen events or "thick tail" risks are likely, as these are the blind spots for purely algorithmic strategies.

To combat unreliable backtests, CFM is building "meta-models" that quantitatively predict whether a new model's results are overfitted. This systematic approach aims to replace human judgment with a data-driven process for deciding if a trading model is robust enough for production.