Author tests four AI assistants on flawed forecasting scenarios
A researcher hid four common pitfalls in a forecasting task—leakage, reporting delays, promotion effects, and structural breaks—and asked four AI assistants to solve it. The assistants included Gemini, DeepSeek, ChatGPT, and Claude. The test was designed to reveal how well each model detects and corrects errors in real-world data scenarios.
Key points
- Four AI assistants—Gemini, DeepSeek, ChatGPT, and Claude—tested on flawed forecasting scenarios with leakage, delays, promotions, and structural breaks
- Author hid traps to measure how well models detect and correct errors in real-world forecasting tasks
- No model identified all traps, but responses varied in accuracy and reasoning
The author did not disclose the exact data or full methodology but described the results as a way to highlight blind spots in AI forecasting tools. While none of the models identified all traps, their responses varied in accuracy and reasoning. The post suggests that AI assistants may still struggle with subtle data issues that human analysts often catch through experience.
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