Are the Futures Computable?

Overview

Published in the Academy of Management Review, this paper examines the boundary between AI's rapidly expanding predictive capabilities and the domain of Knightian uncertainty. AI delivers extraordinary advantages in addressing calculable risk — transforming how ventures identify, gather, analyze, and utilize information from their operating environments. The paper asks what happens at the frontier where these powerful capabilities encounter a fundamentally different kind of problem: genuine uncertainty that manifests through a cascading set of four interrelated challenges — actor ignorance, practical indeterminism, agentic novelty, and competitive recursion.

Contribution to the Research Program

This is the theoretical cornerstone of the Cyborg Entrepreneurship research program. It maps the boundary that makes the relationship between human judgment and machine intelligence so fascinating. If AI could resolve Knightian uncertainty, then AI-augmented entrepreneurship would be a straightforward optimization problem — powerful but conceptually uninteresting. Because the relationship is more complex than that, the cyborg entrepreneur emerges as a genuinely novel kind of agent — one whose capabilities exceed those of either human or machine alone, but whose challenges are also unprecedented. The paper provides the theoretical foundation for understanding the emergent dynamics explored across the lab's research: the Ideator's Dilemma, the expectations game, and the second-order consequences of AI adoption.

Key Insights

• AI's predictive power is transformative for calculable risk — the question is what emerges at the boundary where risk gives way to genuine uncertainty
• Knightian uncertainty is ontological, not epistemological — a feature of the future itself, not a limitation of current tools
• The distinction between risk and uncertainty is not a critique of AI but a map of where the most interesting second-order consequences arise
• Entrepreneurial judgment and AI capability are complementary — the futures that matter most require both
• Understanding this boundary reshapes how we think about human-AI collaboration in entrepreneurial decision-making