Paper Overview
Field: Machine Learning Author: Gaurav Tewari Published: 2026-09-14 arXiv: 2609.15919
Abstract
Artificial intelligence presents firms with an unusual timing problem. The technology frontier is improving rapidly, implementation is partly irreversible, and organization-specific capabilities are accumulated through action. This paper develops a two-period decision model of AI deployment under uncertainty in which a firm chooses among immediate deployment, a limited pilot, and waiting.
- Deployment earns current operating value but exposes the firm to architectural obsolescence.
- Waiting preserves the option to adopt after the frontier is observed.
- Piloting sacrifices current operating value to build organization-specific learning without full commitment.
1. A mean-preserving increase in frontier uncertainty raises the value of waiting and piloting, but has no effect on immediate deployment when its payoff is affine in the frontier. 2. When the deployed architecture captures only a limited share of future improvements, expectations of faster frontier progress can reduce the relative attractiveness of immediate deployment. 3. When the expected value of capabilities built by piloting exceeds its cost, piloting strictly dominates waiting. 4. Sufficiently valuable organization-specific learning creates a non-empty region where the optimal policy is "pilot early, commit late." 5. There exists a closed-form modularity threshold above which immediate deployment dominates the optimal outside option. 6. Production learning and pilot-specific learning have distinct marginal effects on timing.
A continuous-time extension recovers standard results: uncertainty raises the adoption threshold, while capability and modularity lower it.
The paper distinguishes between deployment, experimentation, and waiting, showing why rapid progress can rationally increase experimentation without justifying irreversible commitment.
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