The Strategic Foresight of LLMs: Evidence from a Fully Prospective Venture Tournament

TL;DR

This study demonstrates that large language models can outperform humans in strategic foresight tasks, accurately predicting the success of new ventures before outcomes are known, with some models achieving near 80% accuracy.

Contribution

It provides the first fully prospective evaluation of LLMs in strategic forecasting, showing their superior predictive performance over human experts in real-time venture success prediction.

Findings

LLMs achieved higher rank correlations than humans in predicting venture success.

The best LLM reached a 0.74 rank correlation, correctly ordering 80% of venture pairs.

Ensembles and hybrid teams did not outperform the best standalone LLM.

Abstract

Can artificial intelligence outperform humans at strategic foresight -- the capacity to form accurate judgments about uncertain, high-stakes outcomes before they unfold? We address this question through a fully prospective prediction tournament using live Kickstarter crowdfunding projects. Thirty U.S.-based technology ventures, launched after the training cutoffs of all models studied, were evaluated while fundraising remained in progress and outcomes were unknown. A diverse suite of frontier and open-weight large language models (LLMs) completed 870 pairwise comparisons, producing complete rankings of predicted fundraising success. We benchmarked these forecasts against 346 experienced managers recruited via Prolific and three MBA-trained investors working under monitored conditions. The results are striking: human evaluators achieved rank correlations with actual outcomes between 0.04 and 0.45, while several frontier LLMs exceeded 0.60, with the best (Gemini 2.5 Pro) reaching 0.74 -- correctly ordering nearly four of every five venture pairs. These differences persist across multiple performance metrics and robustness checks. Neither wisdom-of-the-crowd ensembles nor human-AI hybrid teams outperformed the best standalone model.