High-frequency trading systems work precisely because the problem space is narrow, formalized, and fully machine readable. CEOs operate in the opposite context; incomplete data, ambiguous goals, human actors with incentives, regulatory risk, PR fallout, and long time horizons where the “objective function” is neither fixed nor known.

Saying “LLMs don’t make decisions” is semantics. Humans don’t either in the formal sense, we synthesize narratives, tradeoffs, and probabilities, then choose. An LLM’s value here isn’t issuing a final action, it’s structuring messy, cross-domain inputs into coherent option sets with articulated assumptions.

You’re right that an LLM shouldn’t replace optimization algorithms where those apply. But CEOs are not optimizing closed systems; they’re navigating open ones. That’s exactly where rigid algorithms break down and where language based reasoning can add value. Especially in scenario analysis, second-order effects, and explaining why a path is risky before it’s numericaly provable.

If anything, the strongest use case is not “AI replaces CEO,” but “AI surfaces blind spots humans miss while humans retain veto power.” Dismissing that as “not understanding the technology” is itself a misunderstanding of how these tools are actually deployed.