As AI forecasts become cheaper and faster, they’re increasingly treated as replacements for physics‑based weather models. That’s risky—especially for the extreme events that threaten lives, infrastructure, and policy decisions.
In When Speed Becomes a Liability, I argue that the problem isn’t lack of data or better training. It’s mathematical.AI excels at interpolation. Extreme weather lives in the realm of chaos, bifurcations, and tipping points—where extrapolation, causal grounding, and physical laws matter most.
Physics‑based models preserve what AI alone cannot:
• validity under unprecedented conditions
• honest representation of uncertainty
• behavior grounded in the laws of nature, not historical frequency
The right path forward isn’t AI vs physics. It’s hybrid intelligence: physics‑based models as the backbone, with AI accelerating computation, ensembles, and uncertainty—without replacing causal structure.
Weather forecasting is just the test case. The same issue applies to climate policy, medicine, and any high‑stakes domain where failure in the tails is not recoverable.
Read the full paper here:
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6480044
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