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

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.

The authors' own abstract, copied from arxiv.org.

How to cite it

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Csaszar, Peterson, & Wilde (2026). The Strategic Foresight of LLMs: Evidence from a Fully Prospective Venture Tournament. Working paper. https://arxiv.org/abs/2602.01684

Csaszar, , et al. "The Strategic Foresight of LLMs: Evidence from a Fully Prospective Venture Tournament." Working paper, 2026, https://arxiv.org/abs/2602.01684.

@article{csaszar2026the,
  title = {The Strategic Foresight of LLMs: Evidence from a Fully Prospective Venture Tournament},
  author = {Csaszar and Peterson and Wilde},
  journal = {Working paper},
  year = {2026},
  url = {https://arxiv.org/abs/2602.01684},
}

What it is

A February 2026 working paper (Csaszar, Peterson, Wilde) that had AI models and 346 experienced managers rank 30 live Kickstarter tech ventures before their fundraising ended. The best model, Gemini 2.5 Pro, ranked outcomes far more accurately than the humans, and human-AI teams did not beat it.

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