Study: The strategic foresight of LLMs: Evidence from a fully prospective venture tournament
For decades, the idea that artificial intelligence can beat humans at number-crunching tasks like high-frequency trading has been widely accepted.
But strategic foresight-the ability to predict the success of high-stakes, uncertain business ventures-has long been held as a uniquely human superpower.
A study co-authored by a University of Michigan business expert suggests artificial intelligence is beginning to surpass human prediction capabilities in the context of predicting the success of new ventures.
"Strategy felt so different from algorithmic trading," said Felipe Csaszar, a professor of strategy at U-M's Ross School of Business. "It was, in a sense, obvious that algorithmic trading was doable, because it was all about numbers. But strategy is all about words."
The findings could mean businesses no longer need to compete on once rare, specialized strategic forecasting skills, but how they integrate AI-generated predictions.
Significant shift
According to Csaszar, the frontier of AI possibilities in the field of strategy has shifted significantly. To test this theory, he and his co-authors, Aticus Peterson of New York University and Daniel Wilde of Indiana University, conducted a prospective prediction tournament using 30 live crowdfunding projects. These technology ventures were launched after the training cutoffs of the AI models studied, ensuring the models could not use past data in their evaluations.
Various large language models completed 870 pairwise comparisons, producing rankings of predicted fundraising success. These forecasts were benchmarked against the predictions of 346 managers and three investors trained in MBA programs.
In this specific scenario, top-tier LLMs were significantly more accurate than the human experts. While the best human results correctly identified a winner in 3 out of 5 comparisons, the top-performing model, Gemini 2.5 Pro, achieved a correlation of 0.74-correctly identifying the winner in nearly four out of five cases.
The augmentation trap
The research identified a phenomenon dubbed the "augmentation trap," where combining human and AI judgments actually reduced overall accuracy compared to the AI working alone. The addition of human judgment introduces idiosyncratic noise and error that degrades the final result.
"In this case, the wisdom-of-the-crowd logic doesn't produce an improvement in accuracy," Csaszar said. "If you include a human in the mix, performance decreases."
Solving bounded rationality
Csaszar explained AI's success stems from its ability to overcome human's "bounded rationality"-the inherent cognitive limits humans face regarding time, memory and consistency.
"AI is very promising because it relaxes some of these bounds," Csaszar said. "No human has read as much as ChatGPT; no human has as much time to think about each project."
The study also suggests a model's success is related to its performance on Humanity's Last Exam, one of the most difficult benchmarks, which measures AI's graduate-level knowledge on a broad range of subjects.
"The ability to predict what's going to happen requires a broad set of knowledge that you get from knowing about multiple fields and being able to (reason) about those," Csaszar said.
This suggests strategic foresight depends on a model's ability to connect disparate concepts across domains rather than simple pattern matching or data retrieval.
The chess moment?
AI's progress in this research may appear to mirror the 1997 "Deep Blue" moment in chess, which proved that machines could master tasks once thought to require human intuition. However, Csaszar says that while chess has world champions like Garry Kasparov to beat, strategic foresight lacks a clearly identified top-tier competitor to serve as a benchmark.
Because the experiment was limited to a specific setting, Csaszar said, "We are not saying that the 'chess moment' has arrived, but it does appear that what AI can do in strategy has changed."
Cost of cognition
Just as the Industrial Revolution lowered the cost of physical labor and the internet lowered the cost of information, Csaszar noted AI could lower the cost of the high-level reasoning required for strategy.
He says when the cost of cognition drops, it changes what defines a company's "competitive advantage." If the ability to predict the future (foresight) is no longer an expensive, rare human skill but an accessible AI output, firms will have to compete on other things, such as how they integrate those predictions or what unique data they own.
"Cognition is everywhere, so this will have effects everywhere," he said.
Written by Judi Melena Smelser, Ross School of Business
