Joint Optimization of Human Headcount and Stochastic AI Resource Capacity

Generative AI tools are rapidly becoming standard for coding and other business operations. However, while these tools can drive productivity, their unpredictable, and often significant, costs are putting severe financial strain on organizations. The leadership of such organizations must split a fixed budget between human headcount and generative-AI token capacity, yet standard deterministic planning ignores both the heavy-tailed volatility of token consumption and the cognitive cost of auditing AI-generated output. We model the joint allocation by intersecting a cognitive-friction-adjusted production function with a chance-constrained stochastic budget frontier. Per-engineer token usage is treated as i.i.d., non-negative, and right-skewed; no parametric family is assumed, as only its mean, variance, and skewness enter the analysis. The chance constraint is reduced to a deterministic equivalent via the Central Limit Theorem with a Cornish--Fisher skewness correction. Solving the resulting Lagrangian yields a closed-form optimal headcount, a transcendental condition for optimal per-capita token intensity, and a bordered-Hessian second-order condition that holds for any reasonable volatility-to-headcount ratio. Comparative statics show that rising individual token volatility raises optimal per-capita token intensity while contracting headcount: although human labor and tokens are complements in production, the stochastic budget makes them substitutes at the margin. Individual usage skewness acts as a pure deadweight tax that shrinks the budget without altering the substitution dynamics. Both conclusions presuppose that usage dispersion is invariant to the planned mean allocation. In the case where dispersion instead scales proportionally with the mean, the substitution reverses, so which regime applies is a sharp, empirically testable question.