发表机构
Omega Venture Partners（欧米茄风险投资合伙公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文构建两期决策模型，分析快速技术进步下企业AI采纳的时机选择，区分部署、试点与等待，证明“早期试点、后期承诺”在组织学习价值足够高时最优，并阐明不确定性、能力与模块化对采纳阈值的影响。
AI 中文摘要
人工智能给企业带来了一个不寻常的时机问题。技术前沿正在快速进步，实施部分不可逆，而组织特定的能力是通过行动积累的。本文开发了一个在不确定性下进行AI部署的两期决策模型，其中企业可以选择立即部署、有限试点或等待。部署能获得当前运营价值，但使企业面临架构过时的风险；等待保留了在观察前沿后采纳的期权；试点则牺牲当前运营价值，以在不完全承诺的情况下建立组织特定的学习。该模型产生了五个核心时机结果，以及一个关于学习发生地点的比较结果。第一，前沿不确定性的均值保持增加会提高等待和试点的价值，但当立即部署的收益在前沿上是仿射函数时，其价值保持不变。第二，更快的预期前沿进步会降低立即部署的相对吸引力，当部署的架构仅捕获未来改进的有限份额时。第三，当试点所建立的能力的预期价值超过其成本时，试点恰好优于等待。第四，足够有价值的组织特定学习创造了一个非空区域，在该区域中“早期试点，后期承诺”是最优的。第五，存在一个闭式模块化阈值，超过该阈值时，立即部署优于最佳外部选项。第六，生产学习和试点特定学习对时机边际的影响不同。连续时间扩展恢复了标准结果，即不确定性提高采纳阈值，而能力和模块化降低该阈值。本文区分了部署、实验和等待，并展示了为什么快速进步可以理性地增加实验，而不证明不可逆承诺的合理性。
英文摘要
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; a pilot sacrifices current operating value to build organization-specific learning without full commitment. The model yields five central timing results and a sixth comparative result on where learning occurs. First, a mean-preserving increase in frontier uncertainty raises the value of waiting and piloting but leaves immediate deployment unchanged when its payoff is affine in the frontier. Second, faster expected frontier progress can reduce the relative attractiveness of immediate deployment when deployed architecture captures only a limited share of future improvement. Third, a pilot dominates waiting exactly when the expected value of the capability it builds exceeds its cost. Fourth, sufficiently valuable organization-specific learning creates a nonempty region in which "pilot early, commit late" is optimal. Fifth, there is a closed-form modularity threshold above which immediate deployment dominates the best outside option. Sixth, production learning and pilot-specific learning affect the timing margin differently. A continuous-time extension recovers the standard result that uncertainty raises the adoption threshold while capability and modularity lower it. The paper separates deploying, experimenting, and waiting, and shows why rapid progress can rationally increase experimentation without justifying irreversible commitment.
Comments22 pages, 6 figures, 3 tables
